[en] Manufacturing systems are becoming more sophisticated and expensive, particularly with the development of the intelligent industry. The complexity of the architecture and concept of Smart Manufacturing (SM) makes it vulnerable to several faults and failures that impact the entire behavior of the manufacturing system. It is crucial to find and detect any potential anomalies and faults as soon as possible because of the low tolerance for performance deterioration, productivity decline, and safety issues. To overcome these issues, a variety of approaches exist in the literature. However, the multitude of techniques make it difficult to choose the appropriate method in relation to a given context. This paper proposes a new architecture for a conceptual model of intelligent fault diagnosis and self-healing for smart manufacturing systems. Based on this architecture, a review method for the different approaches, sub-approaches and methods used to develop a Fault Detection and Diagnosis (FDD) and Self-Healing-Fault-Tolerant (SH-FT) strategy dedicated to smart manufacturing is defined. Moreover, this paper reviews and analyzes more than 256 scientific articles on fault diagnosis and self-healing approaches and their applications in SM in the last decade. Finally, promising research directions in the field of resilient smart manufacturing are highlighted.
Precision for document type :
Review article
Disciplines :
Electrical & electronics engineering
Author, co-author :
ALDRINI, Joma ✱; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
CHIHI, Ines ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Sidhom, Lilia; Mechanical Department, National School of Engineering of Bizerte, University of Carthage, Carthage, Tunisia ; LAPER, Faculty of Sciences, El Manar University, Tunis, Tunisia
✱ These authors have contributed equally to this work.
External co-authors :
yes
Language :
English
Title :
Fault diagnosis and self-healing for smart manufacturing: a review
Abbas, M., & Zhang, D. J. (2021). A smart fault detection approach for PV modules using adaptive neuro-fuzzy inference framework. Energy Reports, 7, 2962–2975. 10.1016/j.egyr.2021.04.059 DOI: 10.1016/j.egyr.2021.04.059
Abbaspour, A., Mokhtari, S., Sargolzaei, A., & Yen, K. K. (2020). A survey on active fault-tolerant control systems. Electronics, 9(9), 24–1513. 10.3390/electronics9091513 DOI: 10.3390/electronics9091513
Abid, A., Khan, M. T., & Iqbal, J. (2021). A review on fault detection and diagnosis techniques: Basics and beyond. Artificial Intelligence Review, 54(5), 3639–3664. 10.1007/s10462-020-09934-2 DOI: 10.1007/s10462-020-09934-2
Abidi, M. H., Mohammed, M. K., & Alkhalefah, H. (2022). Predictive maintenance planning for industry 4.0 using machine learning for sustainable manufacturing. Sustainability, 14(6), 3387. 10.3390/su14063387 DOI: 10.3390/su14063387
Adhikari, S., Sinha, N., & Dorendrajit, T. (2016). Fuzzy logic based on-line fault detection and classification in transmission line. Springerplus, 5, 14. 10.1186/s40064-016-2669-4 DOI: 10.1186/s40064-016-2669-4
Aguilera, J. J., Meesenburg, W., Ommen, T., Markussen, W. B., Poulsen, J. L., Zuehlsdorf, B., & Elmegaard, B. (2022). A review of common faults in large-scale heat pumps. Renewable & Sustainable Energy Reviews, 168, 17. 10.1016/j.rser.2022.112826 DOI: 10.1016/j.rser.2022.112826
Ahmad, M., & Mohd-Mokhta, R. (2022). A survey on model-based fault detection techniques for linear time-invariant systems with numerical analysis. Pertanika Journal of Science and Technology, 30(1), 53–78. 10.47836/pjst.30.1.04 DOI: 10.47836/pjst.30.1.04
Ahmed, M., Baqqar, M., Gu, F. & Ball, A. D. (2012). Fault detection and diagnosis using principal component analysis of vibration data from a reciprocating compressor [Proceeding Conference]. Proceeding of 2012 UKACC International conference on control. https://doi.org/10.1109/CONTROL.2012.6334674
Al-Jonid, K., Kozlov, A., & Baryshev, N. (2018). Design of an expert system for the prediction and comprehensive diagnosis of CNC machining fault [Proceedings Paper]. Proceeding of the 2018 International conference on modern trends in manufacturing technologies and equipment. https://doi.org/10.1051/matecconf/201822401091
Altisen, K., Devismes, S., Durand, A., Johnen, C., Petit, F., & Acm. (2021). Self-stabilizing systems in spite of high dynamics [Proceedings Paper]. Proceedings of the 22nd International Conference on distributed computing and networking, 156–165. https://doi.org/10.1145/3427796.3427838
Amin, A. A., & Mahmood-ul-Hasan, K. (2021). Robust passive fault tolerant control for air fuel ratio control of internal combustion gasoline engine for sensor and actuator faults. IETE Journal of Research. 10.1080/03772063.2021.1906767 DOI: 10.1080/03772063.2021.1906767
Amin, M. T., Imtiaz, S., & Khan, F. (2018). Process system fault detection and diagnosis using a hybrid technique. Chemical Engineering Science, 189, 191–211. 10.1016/j.ces.2018.05.045 DOI: 10.1016/j.ces.2018.05.045
Angelopoulos, A., Michailidis, E. T., Nomikos, N., Trakadas, P., Hatziefremidis, A., Voliotis, S., & Zahariadis, T. (2020). Tackling faults in the Industry 4.0 era-A survey of machine-learning solutions and key aspects. Sensors, 20(1), 34. 10.3390/s20010109 DOI: 10.3390/s20010109
Anwarsha, A., & Babu, T. N. (2022). A review on the role of tunable Q-factor wavelet transform in fault diagnosis of rolling element bearings. Journal of Vibration Engineering & Technologies. 10.1007/s42417-022-00484-1 DOI: 10.1007/s42417-022-00484-1
Ayvaz, S., & Alpay, K. (2021). Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Systems with Applications, 173, 10. 10.1016/j.eswa.2021.114598 DOI: 10.1016/j.eswa.2021.114598
Babaei, M., Shi, J., & Abdelwahed, S. (2018). A survey on fault detection, isolation, and reconfiguration methods in electric ship power systems. IEEE Access, 6, 9430–9441. 10.1109/access.2018.2798505 DOI: 10.1109/access.2018.2798505
Badri, M., Chihi, I., Sidhom, L., Abdelkrim, A., & Ieee. (2016). Observer approach for fault detection of the handwriting process [Proceedings Paper]. 2016 4th International Conference on Control Engineering & Information Technology (CEIT), 6. https://doi.org/10.1109/CEIT.2016.7929091
Badri, M., Chihi, I., Sidhom, L., & Abdelkrim, A. (2017). Estimation of actuator faults for system with unpredictable inputs [Proceedings Paper]. 2017 International Conference on Control, Automation and Diagnosis (ICCAD), 169–173. https://doi.org/10.1109/CADIAG.2017.8075651
Benosman, M. (2010). A survey of some recent results on nonlinear fault tolerant control. Mathematical Problems in Engineering. 10.1155/2010/586169 DOI: 10.1155/2010/586169
Bensalem, Y., Abbassi, R., & Jerbi, H. (2021). Fuzzy logic based-active fault tolerant control of speed sensor failure for five-phase PMSM. Journal of Electrical Engineering & Technology, 16(1), 287–299. 10.1007/s42835-020-00559-7 DOI: 10.1007/s42835-020-00559-7
Bernardi, E., Pipino, H. A., & Adam, E. J. (2020). Full-order Output Observer Applied to a Linear Parameter Varying System with Unknown Input[Proceedings Paper]. 2020 IEEE Congreso Bienal de Argentina (ARGENCON). https://doi.org/10.1109/ARGENCON49523.2020.9505451
Berredjem, T., & Benidir, M. (2018). Bearing faults diagnosis using fuzzy expert system relying on an improved range overlaps and similarity method. Expert Systems with Applications, 108, 134–142. 10.1016/j.eswa.2018.04.025 DOI: 10.1016/j.eswa.2018.04.025
Blesa, J., Puig, V., Saludes, J., & Fernandez-Canti, R. M. (2016). Set-membership parity space approach for fault detection in linear uncertain dynamic systems. International Journal of Adaptive Control and Signal Processing, 30(2), 186–205. 10.1002/acs.2476 DOI: 10.1002/acs.2476
Bousdekis, A., Lepenioti, K., Apostolou, D., & Mentzas, G. (2019). Decision making in predictive maintenance: Literature review and research agenda for industry 4.0. IFAC Papersonline, 52(13), 607–612. 10.1016/j.ifacol.2019.11.226 DOI: 10.1016/j.ifacol.2019.11.226
Bouzida, A., Touhami, O., Ibtiouen, R., Belouchrani, A., Fadel, M., & Rezzoug, A. (2011). Fault diagnosis in industrial induction machines through discrete wavelet transform. IEEE Transactions on Industrial Electronics, 58(9), 4385–4395. 10.1109/tie.2010.2095391 DOI: 10.1109/tie.2010.2095391
Brito, L. C., Susto, G. A., Brito, J. N., & Duarte, M. A. V. (2022). An explainable artificial intelligence approach for unsupervised fault detection and diagnosis in rotating machinery. Mechanical Systems and Signal Processing, 163, 21. 10.1016/j.ymssp.2021.108105 DOI: 10.1016/j.ymssp.2021.108105
Brkovic, A., Gajic, D., Gligorijevic, J., Savic-Gajic, I., Georgieva, O., & Di Gennaro, S. (2017). Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery. Energy, 136, 63–71. 10.1016/j.energy.2016.08.039 DOI: 10.1016/j.energy.2016.08.039
Calabrese, F., Regattieri, A., Bortolini, M., Gamberi, M., & Pilati, F. (2021). Predictive maintenance: A novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries. Applied Sciences-Basel, 11(8), 28. 10.3390/app11083380 DOI: 10.3390/app11083380
Canizo, M., Onieva, E., Conde, A., Charramendieta, S., & Trujillo, S. (2017). Real-time predictive maintenance for wind turbines using big data frameworks. IEEE International Conference on Prognostics and Health Management (ICPHM), 2017, 70–77. 10.1109/ICPHM.2017.7998308 DOI: 10.1109/ICPHM.2017.7998308
Capriglione, D., Carratu, M., Pietrosanto, A., & Sommella, P. (2018). NARX ANN-based instrument fault detection in motorcycle. Measurement, 117, 304–311. 10.1016/j.measurement.2017.12.026 DOI: 10.1016/j.measurement.2017.12.026
Castresana, J., Gabina, G., Martin, L., Basterretxea, A., & Uriondo, Z. (2022). Marine diesel engine ANN modelling with multiple output for complete engine performance map. Fuel, 319, 13. 10.1016/j.fuel.2022.123873 DOI: 10.1016/j.fuel.2022.123873
Cerone, V. Diego, R., & Abuabiah, M. (2017). Direct data-driven control design through set-membership errors-in-variables identification techniques [Conference Paper]. 2017 American Control Conference (ACC). https://doi.org/10.23919/ACC.2017.7962984
Chadli, M., Davoodi, M., & Meskin, N. (2017). Distributed state estimation, fault detection and isolation filter design for heterogeneous multi-agent linear parameter-varying systems. IET Control Theory and Applications, 11(2), 254–262. 10.1049/iet-cta.2016.0912 DOI: 10.1049/iet-cta.2016.0912
Chen, J., Lim, C. P., Tan, K. H., Govindan, K., & Kumar, A. (2021). Artificial intelligence-based human-centric decision support framework: An application to predictive maintenance in asset management under pandemic environments. Annals of Operations Research. 10.1007/s10479-021-04373-w DOI: 10.1007/s10479-021-04373-w
Chen, J. L., Li, Z. P., Pan, J., Chen, G. G., Zi, Y. Y., Yuan, J., Chen, B. Q., & He, Z. J. (2016a). Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing, 70–71, 1–35. 10.1016/j.ymssp.2015.08.023 DOI: 10.1016/j.ymssp.2015.08.023
Chen, J. L., Pan, J., Li, Z. P., Zi, Y. Y., & Chen, X. F. (2016b). Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals. Renewable Energy, 89, 80–92. 10.1016/j.renene.2015.12.010 DOI: 10.1016/j.renene.2015.12.010
Chen, L., Yan, B., Wang, H., Shao, K., Kurniawan, E., & Wang, G. Y. (2022). Extreme-learning-machine-based robust integral terminal sliding mode control of bicycle robot. Control Engineering Practice, 121, 10. 10.1016/j.conengprac.2022.105064 DOI: 10.1016/j.conengprac.2022.105064
Chen, X. H., Zhang, B. K., & Gao, D. (2021b). Bearing fault diagnosis base on multi-scale CNN and LSTM model. Journal of Intelligent Manufacturing, 32(4), 971–987. 10.1007/s10845-020-01600-2 DOI: 10.1007/s10845-020-01600-2
Cheng, J. C. P., Chen, W. W., Chen, K. Y., & Wang, Q. (2020). Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Automation in Construction, 112, 21. 10.1016/j.autcon.2020.103087 DOI: 10.1016/j.autcon.2020.103087
Cheng, J. F., Chen, W. H., Tao, F., & Lin, C. L. (2018). Industrial IoT in 5G environment towards smart manufacturing. Journal of Industrial Information Integration, 10, 10–19. 10.1016/j.jii.2018.04.001 DOI: 10.1016/j.jii.2018.04.001
Chi, Y. F., Dong, Y. J., Wang, Z. J., Yu, F. R., & Leung, V. C. M. (2022). Knowledge-based fault diagnosis in industrial internet of things: a survey. IEEE Internet of Things Journal, 9(15), 12886–12900. 10.1109/jiot.2022.3163606 DOI: 10.1109/jiot.2022.3163606
Chihi, I., & Benrejeb, M. (2018). Online fault detection approach of unpredictable inputs: Application to handwriting system. Complexity. 10.1155/2018/9789060 DOI: 10.1155/2018/9789060
Ciaburro, G. (2022). Machine fault detection methods based on machine learning algorithms: A review. Mathematical Biosciences and Engineering, 19(11), 11453–11490. 10.3934/mbe.2022534 DOI: 10.3934/mbe.2022534
Cinar, Z. M., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability, 12(19), 42. 10.3390/su12198211 DOI: 10.3390/su12198211
Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: progress, trends, and directions. Sustainability, 12(2), 26. 10.3390/su12020492 DOI: 10.3390/su12020492
Cohen, Y., Faccio, M., & Elaluf, A. (2019). Hierarchy of smart awareness in assembly 4.0 systems. IFAC Papersonline., 52(13), 1508–1512. 10.1016/j.ifacol.2019.11.413 DOI: 10.1016/j.ifacol.2019.11.413
Davis, J., Edgar, T., Porter, J., Bernaden, J., & Sarli, M. (2012). Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Computers & Chemical Engineering, 47, 145–156. 10.1016/j.compchemeng.2012.06.037 DOI: 10.1016/j.compchemeng.2012.06.037
Deng, X. W., Gao, Q. S., Zhang, C., Hu, D., & Yang, T. (2017). Rule - based Fault Diagnosis Expert System for Wind Turbine [Proceedings Paper]. 2017 International Conference on Information Science and Technology (IST), 11(9). https://doi.org/10.1051/itmconf/20171107005
Dhaou, O., Sidhom, L., Chihi, I., & Abdelkrim, A. (2016). On the numerical differentiation problem of noisy signal. Proceedings of Engineering and Technology, 2016, 423–429.
Ding, R. Q., Cheng, M., Jiang, L., & Hu, G. L. (2021). Active fault-tolerant control for electro-hydraulic systems with an independent metering valve against valve faults. IEEE Transactions on Industrial Electronics, 68(8), 7221–7232. 10.1109/tie.2020.3001808 DOI: 10.1109/tie.2020.3001808
Divya, D., Marath, B., & Kumar, M. B. S. (2022). Review of fault detection techniques for predictive maintenance. Journal of Quality in Maintenance Engineering. 10.1108/jqme-10-2020-0107 DOI: 10.1108/jqme-10-2020-0107
Djelloul, I., Sari, Z., & Latreche, K. (2018). Uncertain fault diagnosis problem using neuro-fuzzy approach and probabilistic model for manufacturing systems. Applied Intelligence, 48(9), 3143–3160. 10.1007/s10489-017-1132-8 DOI: 10.1007/s10489-017-1132-8
Du, X., Chen, J. J., Zhang, H. B., & Wang, J. Q. (2022). Fault detection of aero-engine sensor based on inception-CNN. Aerospace, 9(5), 18. 10.3390/aerospace9050236 DOI: 10.3390/aerospace9050236
Duan, F., & Zivanovic, R. (2016). Induction motor stator fault detection by a condition monitoring scheme based on parameter estimation algorithms. Electric Power Components and Systems, 44(10), 1138–1148. 10.1080/15325008.2015.1089336 DOI: 10.1080/15325008.2015.1089336
Elmasry, W., & Wadi, M. (2022). Detection of faults in electrical power grids using an enhanced anomaly-based method. Arabian Journal for Science and Engineering. 10.1007/s13369-022-07030-x DOI: 10.1007/s13369-022-07030-x
Enciso, L., Noack, M., Reger, J., & Perez-Zuniga, G. (2021). Modulating function based fault diagnosis using the parity space method. IFAC Papersonline, 54(7), 268–273. 10.1016/j.ifaco1.2021.08.370 DOI: 10.1016/j.ifaco1.2021.08.370
Eren, L., Ince, T., & Kiranyaz, S. (2019). A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. Journal of Signal Processing Systems for Signal Image and Video Technology, 91(2), 179–189. 10.1007/s11265-018-1378-3 DOI: 10.1007/s11265-018-1378-3
Fan, W., Zhou, Q., Li, J., & Zhu, Z. K. (2018). A wavelet-based statistical approach for monitoring and diagnosis of compound faults with application to rolling bearings. IEEE Transactions on Automation Science and Engineering, 15(4), 1563–1572. 10.1109/tase.2017.2720177 DOI: 10.1109/tase.2017.2720177
Fazai, R., Mansouri, M., Abodayeh, K., Nounou, H., & Nounou, M. (2019). Online reduced kernel PLS combined with GLRT for fault detection in chemical systems. Process Safety and Environmental Protection, 128, 228–243. 10.1016/j.psep.2019.05.018 DOI: 10.1016/j.psep.2019.05.018
Foo, G. H. B., Zhang, X. N., & Vilathgamuwa, D. M. (2013). A sensor fault detection and isolation method in interior permanent-magnet synchronous motor drives based on an extended kalman filter. IEEE Transactions on Industrial Electronics, 60(8), 3485–3495. 10.1109/tie.2013.2244537 DOI: 10.1109/tie.2013.2244537
Fourlas, G. K., & Karras, G. C. (2021). A survey on fault diagnosis and fault-tolerant control methods for unmanned aerial vehicles. Machines, 9(9), 34. 10.3390/machines9090197 DOI: 10.3390/machines9090197
Gao, D. X., Hou, J. J., Liang, K., & Yang, Q. (2018). Fault diagnosis system for electric vehicle charging devices based on fault tree analysis[Proceeding Conference]. 2018 37th Chinese Control Conference (CCC). https://doi.org/10.23919/ChiCC.2018.8482691
Gao, Z. W., Cecati, C., & Ding, S. X. (2015). A survey of fault diagnosis and fault-tolerant techniques-part I: Fault diagnosis with model-based and signal-based approaches. IEEE Transactions on Industrial Electronics, 62(6), 3757–3767. 10.1109/tie.2015.2417501 DOI: 10.1109/tie.2015.2417501
Ghosh, D., Sharman, R., Rao, H. R., & Upadhyaya, S. (2007). Self-healing systems-survey and synthesis. Decision Support Systems, 42(4), 2164–2185. 10.1016/j.dss.2006.06.011 DOI: 10.1016/j.dss.2006.06.011
Goncalves, V. M., Maia, C. A., & Hardouin, L. (2019). On Max-plus linear dynamical system theory: The observation problem. Automatica, 107, 103–111. 10.1016/j.automatica.2019.05.026 DOI: 10.1016/j.automatica.2019.05.026
Gopakumar, P., Mallikajuna, B., Reddy, M. J. B., & Mohanta, D. K. (2018). Remote monitoring system for real time detection and classification of transmission line faults in a power grid using PMU measurements. Protection and Control of Modern Power Systems, 3(1), 10. 10.1186/s41601-018-0089-x DOI: 10.1186/s41601-018-0089-x
Gravanis, G., Dragogias, I., Papakiriakos, K., Ziogou, C., & Diamantaras, K. (2022). Fault detection and diagnosis for non-linear processes empowered by dynamic neural networks. Computers & Chemical Engineering, 156, 10. 10.1016/j.compchemeng.2021.107531 DOI: 10.1016/j.compchemeng.2021.107531
Guezmil, A., Berriri, H., Pusca, R., Sakly, A., Romary, R., & Mimouni, M. F. (2019). Experimental investigation of passove fault tolerant control for induction machine using sliding mode approach. Asian Journal of Control, 21(1), 520–532. 10.1002/asjc.1753 DOI: 10.1002/asjc.1753
Gupta, D., Pratama, M., Ma, Z. Y., Li, J., & Prasad, M. (2019). Financial time series forecasting using twin support vector regression. Plos One, 14(3), 27. 10.1371/journal.pone.0211402 DOI: 10.1371/journal.pone.0211402
Hagh, Y. S., Asl, R. M., Fekih, A., Wu, H. P., & Handroos, H. (2021). Active fault-tolerant control design for actuator fault mitigation in robotic manipulators. IEEE Access, 9, 47912–47929. 10.1109/access.2021.3068448 DOI: 10.1109/access.2021.3068448
Han, S. J., Liu, C. R., Lin, X. Y., Zheng, J. W., Wu, J., & Liu, C. (2020). Dual conductive network hydrogel for a highly conductive, self-healing, anti-freezing, and non-drying strain sensor. ACS Applied Polymer Materials, 2(2), 996–1005. 10.1021/acsapm.9b01198 DOI: 10.1021/acsapm.9b01198
Han, S. Y., Zhou, J., Chen, Y. H., Zhang, Y. F., Tang, G. Y., & Wang, L. (2021). Active fault-tolerant control for discrete vehicle active suspension via reduced-order observer. IEEE Transactions on Systems Man Cybernetics-Systems, 51(11), 6701–6711. 10.1109/tsmc.2020.2964607 DOI: 10.1109/tsmc.2020.2964607
Han, T., Gong, J. C., Yang, X. Q., & An, L. Z. (2022). Fault diagnosis of rolling bearings using dual-tree complex wavelet packet transform and time-shifted multiscale range entropy. IEEE Access, 10, 59308–59326. 10.1109/access.2022.3180338 DOI: 10.1109/access.2022.3180338
Hao, H. Y., Zhang, K., Ding, S. X., Chen, Z. W., & Lei, Y. G. (2014). A data-driven multiplicative fault diagnosis approach for automation processes. ISA Transactions, 53(5), 1436–1445. 10.1016/j.isatra.2013.12.022 DOI: 10.1016/j.isatra.2013.12.022
Harinarayan, R. R. A., & Shalinie, S. M. (2022). XFDDC: EXplainable fault detection diagnosis and correction framework for chemical process systems. Process Safety and Environmental Protection, 165, 463–474. 10.1016/j.psep.2022.07.019 DOI: 10.1016/j.psep.2022.07.019
Harmouche, J., Delpha, C., & Diallo, D. (2015). Incipient fault detection and diagnosis based on Kullback-Leibler divergence using principal component analysis: Part II. Signal Processing, 109, 334–344. 10.1016/j.sigpro.2014.06.023 DOI: 10.1016/j.sigpro.2014.06.023
He, X., Wang, Z. D., Liu, Y., & Zhou, D. H. (2013). Least-squares fault detection and diagnosis for networked sensing systems using a direct state estimation approach. IEEE Transactions on Industrial Informatics, 9(3), 1670–1679. 10.1109/tii.2013.2251891 DOI: 10.1109/tii.2013.2251891
Herve Abdi, L. J. W. (2010). Principal component analysis (Overview). John Wiley & Sons, Inc. 2(4): 433–459. https://doi.org/10.1002/wics.101
Heydarzadeh, M., & Nourani, M. (2016). A two-stage fault detection and isolation platform for industrial systems using residual evaluation. IEEE Transactions on Instrumentation and Measurement, 65(10), 2424–2432. 10.1109/tim.2016.2575179 DOI: 10.1109/tim.2016.2575179
Hosamo, H. H., Svennevig, P. R., Svidt, K., Han, D. G., & Nielsen, H. K. (2022). A digital twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics. Energy and Buildings, 261, 22. 10.1016/j.enbuild.2022.111988 DOI: 10.1016/j.enbuild.2022.111988
Houchati, M., Ben-Brahim, L., Gastli, A., & Meskin, N. (2018). Fault Detection in Modular Multilevel Converter using Principle Component Analysis [Proceedings Paper]. Proceedings 2018 IEEE 12th International Conference on Compatibility, Power Electronics and Power Engineering (Cpe-Powereng 2018), 6. https://doi.org/10.1109/cpe.2018.8372596
Hsu, C. Y., & Liu, W. C. (2021). Multiple time-series convolutional neural network for fault detection and diagnosis and empirical study in semiconductor manufacturing. Journal of Intelligent Manufacturing, 32(3), 823–836. 10.1007/s10845-020-01591-0 DOI: 10.1007/s10845-020-01591-0
Huang, T., Zhang, Q., Tang, X. A., Zhao, S. Y., & Lu, X. N. (2022). A novel fault diagnosis method based on CNN and LSTM and its application in fault diagnosis for complex systems. Artificial Intelligence Review, 55(2), 1289–1315. 10.1007/s10462-021-09993-z DOI: 10.1007/s10462-021-09993-z
Huerta-Rosales, J. R., Granados-Lieberman, D., Garcia-Perez, A., Camarena-Martinez, D., Amezquita-Sanchez, J. P., & Valtierra-Rodriguez, M. (2021). Short-circuited turn fault diagnosis in transformers by using vibration signals, statistical time features, and support vector machines on FPGA. Sensors, 21(11), 29. 10.3390/s21113598 DOI: 10.3390/s21113598
Huynh, K. T., Grall, A., & Berenguer, C. (2019). A parametric predictive maintenance decision-making framework considering improved system health prognosis precision. IEEE Transactions on Reliability, 68(1), 375–396. 10.1109/tr.2018.2829771 DOI: 10.1109/tr.2018.2829771
Jana, D., Patil, J., Herkal, S., Nagarajaiah, S., & Duenas-Osorio, L. (2022). CNN and Convolutional Autoencoder (CAE) based real-time sensor fault detection, localization, and correction. Mechanical Systems and Signal Processing, 169, 30. 10.1016/j.ymssp.2021.108723 DOI: 10.1016/j.ymssp.2021.108723
Jayamaha, D., Lidula, N. W. A., & Rajapakse, A. D. (2019). Wavelet-multi resolution analysis based ANN architecture for fault detection and localization in dc microgrids. IEEE Access, 7, 145371–145384. 10.1109/access.2019.2945397 DOI: 10.1109/access.2019.2945397
Jeon, B. W., Yoon, S. C., & Suk-Hwan, S. (2016). An architecture design for smart manufacturing execution system. Computer-Aided Design and Applications. 10.1080/16864360.2016.1257189 DOI: 10.1080/16864360.2016.1257189
Jiang, Q. C., Yan, X. F., & Zhao, W. X. (2013). Fault detection and diagnosis in chemical processes using sensitive principal component analysis. Industrial & Engineering Chemistry Research, 52(4), 1635–1644. 10.1021/ie3017016 DOI: 10.1021/ie3017016
Jiang, Y. C., & Yin, S. (2018). Recursive total principle component regression based fault detection and its application to vehicular cyber-physical systems. IEEE Transactions on Industrial Informatics, 14(4), 1415–1423. 10.1109/tii.2017.2752709 DOI: 10.1109/tii.2017.2752709
Jiang, Y. C., Yin, S., & Kaynak, O. (2018). Data-driven monitoring and safety control of industrial cyber-physical system: Basics and beyond. IEEE Access, 6, 47374–47384. 10.1109/access.2018.2866403 DOI: 10.1109/access.2018.2866403
Jimenez-Roa, L. A., Heskes, T., Tinga, T., & Stoelinga, M. (2022). Automatic inference of fault tree models via multi-objective evolutionary algorithms. IEEE Transactions on Dependable and Secure ComputinG. 10.1109/TDSC.2022.3203805 DOI: 10.1109/TDSC.2022.3203805
Jin, T. T., Yan, C. L., Chen, C. H., Yang, Z. J., Tian, H. L., & Wang, S. Y. (2021). Light neural network with fewer parameters based on CNN for fault diagnosis of rotating machinery. Measurement, 181, 15. 10.1016/j.measurement.2021.109639 DOI: 10.1016/j.measurement.2021.109639
Jin, X. Z., & He, Y. G. (2017). Finite-time robust fault-tolerant control against actuator faults and saturations. IET Control Theory and Applications, 11(4), 550–556. 10.1049/iet-cta.2016.1144 DOI: 10.1049/iet-cta.2016.1144
Kafeel, A., Aziz, S., Awais, M., Khan, M. A., Afaq, K., Idris, S. A., Alshazly, H., & Mostafa, S. M. (2021). An expert system for rotating machine fault detection using vibration signal analysis. Sensors, 21(22), 15. 10.3390/s21227587 DOI: 10.3390/s21227587
Kamga Sagoun, W. E., Chihi, I., Sidhom, L., Trabelsi, M., & Georghiou, G. E. (2021). Comparative study on fault classifications techniques for grid-connected PV systems. In International Conference on Electrical Computer and Energy Technologies (ICECET).
Kang, H. S., Lee, J. Y., Choi, S., Kim, H., Park, J. H., Son, J. Y., Kim, B. H., & Noh, S. D. (2016). Smart manufacturing: Past research, present findings, and future directions. International Journal of Precision Engineering and Manufacturing-Green Technology, 3(1), 111–128. 10.1007/s40684-016-0015-5 DOI: 10.1007/s40684-016-0015-5
Khalil, A., Aljanaideh, K. F., & Al Janaideh, M. (2022). Output-only measurements for fault detection of multi-actuator systems in motion control applications. IEEE Sensors Journal, 22(5), 4164–4174. 10.1109/jsen.2022.3141932 DOI: 10.1109/jsen.2022.3141932
Khorsheed, R. M., & Beyca, O. F. (2021). An integrated machine learning: Utility theory framework for real-time predictive maintenance in pumping systems. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture, 235(5), 887–901. 10.1177/0954405420970517 DOI: 10.1177/0954405420970517
Kiangala, K. S., & Wang, Z. H. (2020). An effective predictive maintenance framework for conveyor motors using dual time-series imaging and convolutional neural network in an industry 4.0 environment. IEEE Access, 8, 121033–121049. 10.1109/access.2020.3006788 DOI: 10.1109/access.2020.3006788
Kim, T. H., Jeong, J., & Kim, Y. (2019). A Conceptual Model of Smart Manufacturing Execution System for Rolling Stock Manufacturer [Proceedings Paper]. 2019 10th International Conference on Ambient Systems, Networks and Technologies (ICASNT 2019) / the 2nd International Conference on Emerging Data and Industry 4.0 (ICEDI40 2019) / Affiliated Workshops, 151, 600–606. https://doi.org/10.1016/j.procs.2019.04.081
Kommuri, S. K., Defoort, M., Karimi, H. R., & Veluvolu, K. C. (2016). A robust observer-based sensor fault-tolerant control for pmsm in electric vehicles. IEEE Transactions on Industrial Electronics, 63(12), 7671–7681. 10.1109/tie.2016.2590993 DOI: 10.1109/tie.2016.2590993
Kordestani, M., Salahshoor, K., Safavi, A. A., Saif, M., & Ieee. (2018). An Adaptive Passive Fault Tolerant Control System for a Steam Turbine Using a PCA Based Inverse Neural Network Control Strategy [Proceedings Paper]. 2018 World Automation Congress (WAC). https://doi.org/10.23919/WAC.2018.8430414
Kordestani, M., Salahshoor, K., Safavi, A. A., Saif, M., & Ieee. (2018). An Adaptive Passive Fault Tolerant Control System for a Steam Turbine Using a PCA Based Inverse Neural Network Control Strategy [Proceedings Paper]. 2018 World Automation Congress (WAC). https://doi.org/10.23919/WAC.2018.8430414
Kou, L. L., Qin, Y., Zhao, X. J., & Chen, X. A. (2020). A multi-dimension end-to-end cnn model for rotating devices fault diagnosis on high-speed train bogie. IEEE Transactions on Vehicular Technology, 69(3), 2513–2524. 10.1109/tvt.2019.2955221 DOI: 10.1109/tvt.2019.2955221
Kurukuru, V. S. B., Blaabjerg, F., Khan, M. A., & Haque, A. (2020). A novel fault classification approach for photovoltaic systems. Energies, 13(2), 17. 10.3390/en13020308 DOI: 10.3390/en13020308
Kusiak, A. (2018). Smart manufacturing. International Journal of Production Research, 56(1–2), 508–517. 10.1080/00207543.2017.1351644 DOI: 10.1080/00207543.2017.1351644
Lamouchi, R., Amairi, M., Raissi, T., & Aoun, M. (2022). Active fault tolerant control using zonotopic techniques for linear parameter varying systems: Application to wind turbine system. European Journal of Control, 67, 13. 10.1016/j.ejcon.2022.100700 DOI: 10.1016/j.ejcon.2022.100700
Lamouchi, R., Raissi, T., Amairi, M., & Aoun, M. (2022). Interval observer-based methodology for passive fault tolerant control of linear parameter-varying systems. Transactions of the Institute of Measurement and Control, 44(5), 986–999. 10.1177/01423312211040370 DOI: 10.1177/01423312211040370
Lee, C., Alena, R. L., & Robinson, P. (2005). Migrating fault trees to decision trees for real time fault detection on International Space Station [Proceeding Conference]. 2005 IEEE Aerospace Conference Proceedings, Vols 1–4. https://doi.org/10.1109/AERO.2005.1559584
Lee, C. Y., Wen, M. S., Zhuo, G. L., & Le, T. A. (2022). Application of ANN in induction-motor fault-detection system established with MRA and CFFS. Mathematics., 10(13), 17. 10.3390/math10132250 DOI: 10.3390/math10132250
Li, B., & Chen, X. F. (2014). Wavelet-based numerical analysis: A review and classification. Finite Elements in Analysis and Design, 81, 14–31. 10.1016/j.finel.2013.11.001 DOI: 10.1016/j.finel.2013.11.001
Li, B., Han, T., & Kang, F. Y. (2013). Fault diagnosis expert system of semiconductor manufacturing equipment using a Bayesian network. International Journal of Computer Integrated Manufacturing, 26(12), 1161–1171. 10.1080/0951192x.2013.812803 DOI: 10.1080/0951192x.2013.812803
Li, D. L., Wang, Y., Wang, J. X., Wang, C., & Duan, Y. Q. (2020). Recent advances in sensor fault diagnosis: A review. Sensors and Actuators a-Physical., 309, 14. 10.1016/j.sna.2020.111990 DOI: 10.1016/j.sna.2020.111990
Li, H. Y., Gao, Y., Shi, P., & Lam, H. K. (2016). Observer-based fault detection for nonlinear systems with sensor fault and limited communication capacity. IEEE Transactions on Automatic Control, 61(9), 2745–2751. 10.1109/tac.2015.2503566 DOI: 10.1109/tac.2015.2503566
Li, J., Liu, Y. B., & Li, Q. J. (2022). Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method. Measurement, 189, 15. 10.1016/j.measurement.2021.110500 DOI: 10.1016/j.measurement.2021.110500
Li, X. (2023). Remaining useful life prediction with partial sensor malfunctions using deep adversarial networks. IEEE/CAA Journal of Automatica Sinica, 10, 121–134. 10.1109/JAS.2022.105935 DOI: 10.1109/JAS.2022.105935
Li, X. G., Li, Y., Zhang, Y. Z., Liu, F., & Fang, Y. (2020). Fault diagnosis of belt conveyor based on support vector machine and grey wolf optimization. Mathematical Problems in Engineering. 10.1155/2020/1367078 DOI: 10.1155/2020/1367078
Li, Y. Y., Karimi, H. R., Zhong, M. Y., Ding, S. X., & Liu, S. (2018). Fault detection for linear discrete time-varying systems with multiplicative noise: The finite-horizon case. IEEE Transactions on Circuits and Systems I-Regular Papers, 65(10), 3492–3505. 10.1109/tcsi.2018.2832229 DOI: 10.1109/tcsi.2018.2832229
Li, Z., Wang, Y., & Wang, K. S. (2017). Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario. Advances in Manufacturing., 5(4), 377–387. 10.1007/s40436-017-0203-8 DOI: 10.1007/s40436-017-0203-8
Liang, X. H., Zuo, M. J., & Feng, Z. P. (2018). Dynamic modeling of gearbox faults: A review. Mechanical Systems and Signal Processing, 98, 852–876. 10.1016/j.ymssp.2017.05.024 DOI: 10.1016/j.ymssp.2017.05.024
Liao, J. F., Yuan, H. H., Song, W., Gu, J. S., & Ieee. (2021). Adaptive robust fault detection and control for injection machine mold closing process with accurate parameter estimations [Proceedings Paper]. 2021 IEEE International Conference on Mechatronics (ICM). https://doi.org/10.1109/icm46511.2021.9385687
Lin, S. L. (2021). Application of machine learning to a medium gaussian support vector machine in the diagnosis of motor bearing faults. Electronics. 10.3390/electronics10182266 DOI: 10.3390/electronics10182266
Lin, Z. D., Duan, D. L., Yang, Q., Hong, X. M., Cheng, X., Yang, L. Q., & Cui, S. G. (2020). One-class classifier based fault detection in distribution systems with varying penetration levels of distributed energy resources. IEEE Access, 8, 130023–130035. 10.1109/access.2020.3009385 DOI: 10.1109/access.2020.3009385
Liu, H. Y., Wang, X., Cao, Y. X., Yang, Y. Y., Yang, Y. T., Gao, Y. F., Ma, Z. S., Wang, J. F., Wang, W. J., & Wu, D. C. (2020). Freezing-tolerant, highly sensitive strain and pressure sensors assembled from ionic conductive hydrogels with dynamic cross-links. ACS Applied Materials & Interfaces, 12(22), 25334–25344. 10.1021/acsami.0c06067 DOI: 10.1021/acsami.0c06067
Liu, J. (2020). A dynamic modelling method of a rotor-roller bearing-housing system with a localized fault including the additional excitation zone. Journal of Sound and Vibration, 469, 18. 10.1016/j.jsv.2019.115144 DOI: 10.1016/j.jsv.2019.115144
Liu, J. J., Zhang, M., Wang, H., Zhao, W., & Liu, Y. (2019). Sensor fault detection and diagnosis method for AHU using 1-D CNN and clustering analysis. Computational Intelligence and Neuroscience. 10.1155/2019/5367217 DOI: 10.1155/2019/5367217
Liu, M., & Shi, P. (2013). Sensor fault estimation and tolerant control for Ito stochastic systems with a descriptor sliding mode approach. Automatica, 49(5), 1242–1250. 10.1016/j.automatica.2013.01.030 DOI: 10.1016/j.automatica.2013.01.030
Luo, W. J., Zhang, C., & Jaimoukha, I. M. (2022). Sensor failure-tolerant observer design with regional pole placement. IEEE Control Systems Letters, 6, 2102–2107. 10.1109/lcsys.2021.3138054 DOI: 10.1109/lcsys.2021.3138054
Lyu, P., Zhang, K. W., Yu, W. B., Wang, B. C., & Liu, C. (2022). A novel RSG-based intelligent bearing fault diagnosis method for motors in high-noise industrial environment. Advanced Engineering Informatics, 52, 16. 10.1016/j.aei.2022.101564 DOI: 10.1016/j.aei.2022.101564
Machado, C. G., Winroth, M. P., & da Silva, E. (2020). Sustainable manufacturing in industry 4.0: an emerging research agenda. International Journal of Production Research, 58(5), 1462–1484. 10.1080/00207543.2019.1652777 DOI: 10.1080/00207543.2019.1652777
MacLeod, J., Tan, S., & Moinuddin, K. (2020). Reliability of fire (point) detection system in office buildings in Australia-A fault tree analysis. Fire Safety Journal, 115, 11. 10.1016/j.firesaf.2020.103150 DOI: 10.1016/j.firesaf.2020.103150
Maheswari, R. U., & Umamaheswari, R. (2020). Wind turbine drivetrain expert fault detection system: multivariate empirical mode decomposition based multi-sensor fusion with bayesian learning classification. Intelligent Automation and Soft Computing, 26(3), 479–488. 10.32604/iasc.2020.013924 DOI: 10.32604/iasc.2020.013924
Malhotra, A., Minhas, A. S., Singh, S., Zuo, M. J., Kumar, R., & Kankar, P. K. (2021). Bearing fault diagnosis based on flexible analytical wavelet transform and fuzzy entropy approach [Proceeding Paper]. 2021 1st International Conference on Energy, Materials Sciences and Mechanical Engineering (ICEMSME). Materials Today-Proceedings, 43: 629–635. https://doi.org/10.1016/j.matpr.2020.12.160
Mann, G., & Hwang, I. (2013). State estimation and fault detection and identification for constrained stochastic linear hybrid systems. IET Control Theory and Applications, 7(1), 1–15. 10.1049/iet-cta.2011.0315 DOI: 10.1049/iet-cta.2011.0315
Mansouri, M., Trabelsi, M., Nounou, H., & Nounou, M. (2021). Deep learning-based fault diagnosis of photovoltaic systems: A comprehensive review and enhancement prospects. IEEE Access, 9, 126286–126306. 10.1109/access.2021.3110947 DOI: 10.1109/access.2021.3110947
Mekki, H., Benzineb, O., Boukhetala, D., Tadjine, M., & Benbouzid, M. (2015). Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems. ISA Transactions, 57, 340–351. 10.1016/j.isatra.2015.02.004 DOI: 10.1016/j.isatra.2015.02.004
Melo, A., Camara, M. M., Clavijo, N., & Pinto, J. C. (2022). Open benchmarks for assessment of process monitoring and fault diagnosis techniques: A review and critical analysis. Computers & Chemical Engineering, 165, 42. 10.1016/j.compchemeng.2022.107964 DOI: 10.1016/j.compchemeng.2022.107964
Miljković, D. (2011). Fault Detection Methods: A Literature Survey [Conference paper]. 2011 Proceedings of the 34th International Convention MIPRO
Mittal, S., Khan, M. A., Romero, D., & Wuest, T. (2018). A critical review of smart manufacturing & Industry 4.0 maturity models: Implications for small and medium-sized enterprises (SMEs). Journal of Manufacturing Systems, 49, 194–214. 10.1016/j.jmsy.2018.10.005 DOI: 10.1016/j.jmsy.2018.10.005
Morra, J. H., Tu, Z. W., Apostolova, L. G., Green, A. E., Toga, A. W., & Thompson, P. M. (2010). Comparison of AdaBoost and support vector machines for detecting alzheimer’s disease through automated hippocampal segmentation. IEEE Transactions on Medical Imaging, 29(1), 30–43. 10.1109/tmi.2009.2021941 DOI: 10.1109/tmi.2009.2021941
Mrazgua, J., Tisssir, E., & Ouahi, M. (2019). Fuzzy Fault-Tolerant H-infinity Control Approach for Nonlinear Active Suspension Systems with Actuator Failure [Proceedings Paper]. 2018 Second International Conference on Intelligent Computing in Data Sciences (Icds2018), 148: 465–474. https://doi.org/10.1016/j.procs.2019.01.059
Nasiri, A., Nguang, S. K., Swain, A., & Almakhles, D. (2019). Passive actuator fault tolerant control for a class of MIMO nonlinear systems with uncertainties. International Journal of Control, 92(3), 693–704. 10.1080/00207179.2017.1367102 DOI: 10.1080/00207179.2017.1367102
Nasiri, S., & Khosravani, M. R. (2019). Faults and failures prediction in injection molding process. International Journal of Advanced Manufacturing Technology, 103(5–8), 2469–2484. 10.1007/s00170-019-03699-x DOI: 10.1007/s00170-019-03699-x
Nasser, A. R., Azar, A. T., Humaidi, A. J., Al-Mhdawi, A. K., & Ibraheem, I. K. (2021). Intelligent fault detection and identification approach for analog electronic circuits based on fuzzy logic classifier. Electronics., 10(23), 14. 10.3390/electronics10232888 DOI: 10.3390/electronics10232888
Navi, M., Davoodi, M., Meskin, N., & Ieee. (2015). Sensor Fault Detection and Isolation of an Autonomous Underwater Vehicle Using Partial Kernel PCA [Proceedings Paper]. 2015 IEEE Conference on Prognostics and Health Management, 9. https://doi.org/10.1109/ICPHM.2015.7245022
Ng, Y. S., & Srinivasan, R. (2010). Multi-agent based collaborative fault detection and identification in chemical processes. Engineering Applications of Artificial Intelligence, 23(6), 934–949. 10.1016/j.engappai.2010.01.026 DOI: 10.1016/j.engappai.2010.01.026
Nguyen, K. T. P., & Medjaher, K. (2019). A new dynamic predictive maintenance framework using deep learning for failure prognostics. Reliability Engineering & System Safety, 188, 251–262. 10.1016/j.ress.2019.03.018 DOI: 10.1016/j.ress.2019.03.018
Nor, N. M., Hussain, M. A., & Hassan, C. R. C. (2017). Fault diagnosis and classification framework using multi-scale classification based on kernel Fisher discriminant analysis for chemical process system. Applied Soft Computing, 61, 959–972. 10.1016/j.asoc.2017.09.019 DOI: 10.1016/j.asoc.2017.09.019
Nor, N. M., Hussain, M. A., & Hassan, C. R. C. (2020). Multi-scale kernel Fisher discriminant analysis with adaptive neuro-fuzzy inference system (ANFIS) in fault detection and diagnosis framework for chemical process systems. Neural Computing & Applications, 32(13), 9283–9297. 10.1007/s00521-019-04438-9 DOI: 10.1007/s00521-019-04438-9
Okada, K. F. A., de Morais, A. S., Oliveira-Lopes, L. C., & Ribeiro, L. (2021, Aug 15–18). A Survey on Fault Detection and Diagnosis Methods [Proceeding Paper]. 2021 14th IEEE International Conference on Industry Applications. https://doi.org/10.1109/INDUSCON51756.2021.9529495
Okafor, N. U., & Delaney, D. T. (2021). Missing data imputation on iot sensor networks: Implications for on-site sensor calibration. IEEE Sensors Journal, 21(20), 22833–22845. 10.1109/jsen.2021.3105442 DOI: 10.1109/jsen.2021.3105442
Oliveira, J. C. M., Pontes, K. V., Sartori, I., & Embirucu, M. (2017). Fault Detection and Diagnosis in dynamic systems using Weightless Neural Networks. Expert Systems with Applications, 84, 200–219. 10.1016/j.eswa.2017.05.020 DOI: 10.1016/j.eswa.2017.05.020
Ortiz, L., Gonzalez, J. W., Gutierrez, L. B., & Llanes-Santiago, O. (2020). A review on control and fault-tolerant control systems of AC/DC microgrids. Heliyon, 6(8), 17. 10.1016/j.heliyon.2020.e04799 DOI: 10.1016/j.heliyon.2020.e04799
Pan, H. H., He, X. X., Tang, S., & Meng, F. M. (2018). An improved bearing fault diagnosis method using one-dimensional CNN and LSTM. Strojniski Vestnik-Journal of Mechanical Engineering, 64(7–8), 443–452. 10.5545/sv-jme.2018.5249 DOI: 10.5545/sv-jme.2018.5249
Park, Y. J., Fan, S. K. S., & Hsu, C. Y. (2020). A review on fault detection and process diagnostics in industrial processes. Processes, 8(9), 26. 10.3390/pr8091123 DOI: 10.3390/pr8091123
Peng, D. D., Liu, Z. L., Wang, H., Qin, Y., & Jia, L. M. (2019). A novel deeper one-dimensional CNN with residual learning for fault diagnosis of wheelset bearings in high-speed trains. IEEE Access, 7, 10278–10293. 10.1109/access.2018.2888842 DOI: 10.1109/access.2018.2888842
Peng, D. D., Wang, H., Liu, Z. L., Zhang, W., Zuo, M. J., & Chen, J. (2020). Multibranch and multiscale CNN for fault diagnosis of wheelset bearings under strong noise and variable load condition. IEEE Transactions on Industrial Informatics, 16(7), 4949–4960. 10.1109/tii.2020.2967557 DOI: 10.1109/tii.2020.2967557
Peng, J. Y., Kimmig, A., Wang, D. K., Niu, Z. B., Zhi, F., Wang, J. H., Liu, X. F., & Ovtcharova, J. (2022). A systematic review of data-driven approaches to fault diagnosis and early warning. Journal of Intelligent Manufacturing. 10.1007/s10845-022-02020-0 DOI: 10.1007/s10845-022-02020-0
Phan, T. L. J., Gehrhardt, I., Heik, D., Bahrpeyma, F., & Reichelt, D. (2022). A systematic mapping study on machine learning techniques applied for condition monitoring and predictive maintenance in the manufacturing sector. Logistics-Basel, 6(2), 22. 10.3390/logistics6020035 DOI: 10.3390/logistics6020035
Pignati, M., Zanni, L., Romano, P., Cherkaoui, R., & Paolone, M. (2017). Fault detection and faulted line identification in active distribution networks using synchrophasors-based real-time state estimation. IEEE Transactions on Power Delivery, 32(1), 381–392. 10.1109/tpwrd.2016.2545923 DOI: 10.1109/tpwrd.2016.2545923
Piltan, F., & Kim, J. M. (2018). Bearing fault diagnosis by a robust higher-order super-twisting sliding mode observer. Sensors, 18(4), 22. 10.3390/s18041128 DOI: 10.3390/s18041128
Pisano, A., & Usai, E. (2011). Sliding mode control: A survey with applications in math. Mathematics and Computers in Simulation, 81(5), 954–979. 10.1016/j.matcom.2010.10.003 DOI: 10.1016/j.matcom.2010.10.003
Poursaeidi, M. H., & Kundakcioglu, O. E. (2014). Robust support vector machines for multiple instance learning. Annals of Operations Research, 216(1), 205–227. 10.1007/s10479-012-1241-z DOI: 10.1007/s10479-012-1241-z
Psaier, H., & Dustdar, S. (2011). A survey on self-healing systems: Approaches and systems. Computing, 91(1), 43–73. 10.1007/s00607-010-0107-y DOI: 10.1007/s00607-010-0107-y
Qi, Q. L., & Tao, F. (2018). Digital twin and big data towards smart manufacturing and industry 4.0: 360 degree comparison. IEEE Access, 6, 3585–3593. 10.1109/access.2018.2793265 DOI: 10.1109/access.2018.2793265
Qian, H. M., Qiu, Z. B., & Wu, Y. H. (2017). Robust extended Kalman filtering for nonlinear stochastic systems with random sensor delays, packet dropouts and correlated noises. Aerospace Science and Technology, 66, 249–261. 10.1016/j.ast.2017.03.020 DOI: 10.1016/j.ast.2017.03.020
Qin, Z. J., & Lu, Y. Q. (2021). Self-organizing manufacturing network: A paradigm towards smart manufacturing in mass personalization. Journal of Manufacturing Systems, 60, 35–47. 10.1016/j.jmsy.2021.04.016 DOI: 10.1016/j.jmsy.2021.04.016
Qu, F. M., Liu, J. H., Zhu, H. F., & Zhou, B. W. (2020). Wind turbine fault detection based on expanded linguistic terms and rules using non-singleton fuzzy logic. Applied Energy, 262, 13. 10.1016/j.apenergy.2019.114469 DOI: 10.1016/j.apenergy.2019.114469
Radziwon, A., Bilberg, A., Bogers, M., & Madsen, E. S. (2014). The Smart Factory: Exploring Adaptive and Flexible Manufacturing Solutions [Proceedings Paper]. 24th Daaam International Symposium on Intelligent Manufacturing and Automation, 2013, 69: 1184–1190. https://doi.org/10.1016/j.proeng.2014.03.108
Rahman, O., Wani, S. A., Parveen, S., Khan, S. A., & Ieee. (2019). Detection of Incipient Fault in Transformer using DGA Based Integrated Intelligent Method [Proceedings Paper]. 2019 International Conference on Power Electronics, Control and Automation (ICPECA-2019), 123–128. https://doi.org/10.1109/ICPECA47973.2019.8975638
Rai, A., & Upadhyay, S. H. (2016). A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings. Tribology International, 96, 289–306. 10.1016/j.triboint.2015.12.037 DOI: 10.1016/j.triboint.2015.12.037
Rajput, P. K., & Sikka, G. (2021). Multi-agent architecture for fault recovery in self-healing systems. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2849–2866. 10.1007/s12652-020-02443-8 DOI: 10.1007/s12652-020-02443-8
Reddy, K. H., & Raju, S. G. (2020). Enhancing energy system security and stability - a novel approach using PSO support islanding detection in a grid connected distributed energy system and environmental effects of grid faults. Energy Sources Part a-Recovery Utilization and Environmental Effects. 10.1080/15567036.2020.1795315 DOI: 10.1080/15567036.2020.1795315
Richardson, W. B., Meyer, J., Von Solms, S., & Ieee. (2021). Towards Machine Learning and Low Data Rate IoT for Fault Detection in Data Driven Predictive Maintenance [Proceedings Paper]. 2021 IEEE World AI IoT Congress (AIIoT), 202-208. https://doi.org/10.1109/aiiot52608.2021.9454190
Rotondo, D., Lopez-Estrada, F. R., Nejjari, F., Ponsart, J. C., Theilliol, D., & Puig, V. (2016). Actuator multiplicative fault estimation in discrete-time LPV systems using switched observers. Journal of the Franklin Institute-Engineering and Applied Mathematics, 353(13), 3176–3191. 10.1016/j.jfranklin.2016.06.007 DOI: 10.1016/j.jfranklin.2016.06.007
Rudin, K., Ducard, G. J. J., & Siegwart, R. Y. (2020). Active fault-tolerant control with imperfect fault detection information: Applications to UAVs. IEEE Transactions on Aerospace and Electronic Systems, 56(4), 2792–2805. 10.1109/taes.2019.2959928 DOI: 10.1109/taes.2019.2959928
Saeed, U., Jan, S. U., Lee, Y. D., & Koo, I. (2021). Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliability Engineering & System Safety, 205, 11. 10.1016/j.ress.2020.107284 DOI: 10.1016/j.ress.2020.107284
Safarinejadian, B., Ghane, P., & Monirvaghefi, H. (2015). Fault detection in non-linear systems based on type-2 fuzzy logic. International Journal of Systems Science, 46(3), 394–404. 10.1080/00207721.2013.784371 DOI: 10.1080/00207721.2013.784371
Saravanan, N., & Ramachandran, K. I. (2010). Incipient gear box fault diagnosis using discrete wavelet transform (DWT) for feature extraction and classification using artificial neural network (ANN). Expert Systems with Applications, 37(6), 4168–4181. 10.1016/j.eswa.2009.11.006 DOI: 10.1016/j.eswa.2009.11.006
Schneider, C., Barker, A., & Dobson, S. (2015). A survey of self-healing systems frameworks. Software-Practice & Experience, 45(10), 1375–1398. 10.1002/spe.2250 DOI: 10.1002/spe.2250
Schubert, U., Kruger, U., Arellano-Garcia, H., Feital, T. D., & Wozny, G. (2011). Unified model-based fault diagnosis for three industrial application studies. Control Engineering Practice, 19(5), 479–490. 10.1016/j.conengprac.2011.01.009 DOI: 10.1016/j.conengprac.2011.01.009
Sedighi, T., Phillips, P., & Foote, P. D. (2013). Model-based intermittent fault detection [Proceedings Paper]. 2nd International through-Life Engineering Services Conference, 11, 68–73. https://doi.org/10.1016/j.procir.2013.07.065
Shcherbakov, M., & Sai, C. (2022). A hybrid deep learning framework for intelligent predictive maintenance of cyber-physical systems. ACM Transactions on Cyber-Physical Systems, 6(2), 22. 10.1145/3486252 DOI: 10.1145/3486252
Shen, Q., Yue, C., Goh, C. H., & Wang, D. W. (2019). Active fault-tolerant control system design for spacecraft attitude maneuvers with actuator saturation and faults. IEEE Transactions on Industrial Electronics, 66(5), 3763–3772. 10.1109/tie.2018.2854602 DOI: 10.1109/tie.2018.2854602
Shi Hong, W. J. (2011). Fault Diagnosis of Aeroengine Sensor Based on Support Vector Machine[Proceeding Conference]. 2011 Third international conference on Measuring Technology and Mechatronics Automation., 186–189 https://doi.org/10.1109/ICMTMA.2011.334
Shraim, H., Awada, A., & Youness, R. (2018). A survey on quadrotors: Configurations, modeling and identification, control, collision avoidance, fault diagnosis and tolerant control. IEEE Aerospace and Electronic Systems Magazine, 33(7), 14–33. 10.1109/maes.2018.160246 DOI: 10.1109/maes.2018.160246
Sidhom, L., Chihi, I., & Kamavuako, E. N. (2021). Software sensor to enhance online parametric identification for nonlinear closed-loop systems for robotic applications. Sensors, 21(11), 21. 10.3390/s21113653 DOI: 10.3390/s21113653
Sidhom, L., Chihi, I., Sahraoui, S., Abdelkrim, A., & Ieee. (2016). Intelligent-PI controller for electro-hydraulic system [Proceedings Paper]. 2016 4th International Conference on Control Engineering & Information Technology. https://doi.org/10.1109/CEIT.2016.7929067
Sidhom, L., Chihi, I., & Smaoui, M. (2022). Automation of a hybrid control for electrohydraulic servo-actuators with residual dynamics. Applied Sciences-Basel, 12(6), 19. 10.3390/app12062856 DOI: 10.3390/app12062856
Sidhom, L., Chihi, I., Trabelsi, M., Abu-Rub, H., & Ieee. (2018). Dynamic Gains Robust Differentiator based Fault Detection Approach for Cascaded H-Bridge Multilevel Inverters [Proceedings Paper]. 2018 44th Annual Conference of the IEEE Industrial Electronics Society, 3883–3888. https://doi.org/10.1109/IECON.2018.8591481
Sidhom L., D. O., Chihi I, HidouriI D. and Abdlekrim (2017). Estimator parameters for process faults detection with unpredictable inputs [Conference Paper]. 2017 IEEE International Conference on Control, Automation and Diagnosis.
Sinitsin, V., Ibryaeva, O., Sakovskaya, V., & Eremeeva, V. (2022). Intelligent bearing fault diagnosis method combining mixed input and hybrid CNN-MLP model. Mechanical Systems and Signal Processing, 180, 20. 10.1016/j.ymssp.2022.109454 DOI: 10.1016/j.ymssp.2022.109454
Singh, V., Gangsar, P., Porwal, R., & Atulkar, A. (2023). Artificial intelligence application in fault diagnostics of rotating industrial machines: A state-of-the-art review. Journal of Intelligent Manufacturing, 34(3), 931–960. 10.1007/s10845-021-01861-5 DOI: 10.1007/s10845-021-01861-5
Skliros, C., Esperon Miguez, M., Fakhre, A., & Jennions, I. (2019). A review of model based and data driven methods targeting hardware systems diagnostics. Diagnostyka, 20(1), 3–21. 10.29354/diag/99603 DOI: 10.29354/diag/99603
Soto, E. A. R., Lima, S. L., Saavedra, O. R., & Ieee. (2019). Incipient Fault Diagnosis in Power Transformers by DGA using a Machine Learning ANN - Mean Shift Approach [Proceedings Paper]. 2019 IEEE International Autumn Meeting on Power, Electronics and Computing, 6. https://doi.org/10.1109/ROPEC48299.2019.9057143
Stefanovski, J. D. (2018). Passive fault tolerant perfect tracking with additive faults. Automatica, 87, 432–436. 10.1016/j.automatica.2017.09011 DOI: 10.1016/j.automatica.2017.09011
Sun, B. W., Wang, J. Q., He, Z. M., Qin, Y. R., Wang, D. Y., & Zhou, H. Y. (2019). Fault detection for closed-loop control systems based on parity space transformation. IEEE Access, 7, 75153–75165. 10.1109/access.2019.2916785 DOI: 10.1109/access.2019.2916785
Sun, T. S., Wang, Y. S., & Ding, M. T. (2022). A novel method for actuator fault detection considering complex operating processes. Journal of Process Control, 110, 1–10. 10.1016/j.jprocont.2021.12.003 DOI: 10.1016/j.jprocont.2021.12.003
Sun, Y. N., Qin, W., Zhuang, Z. L., & Xu, H. W. (2021). An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference. Journal of Intelligent Manufacturing, 32(7), 2007–2021. 10.1007/s10845-021-01752-9 DOI: 10.1007/s10845-021-01752-9
Talebi, H. A., & Khorasani, K. (2013). A neural network-based multiplicative actuator fault detection and isolation of nonlinear systems. IEEE Transactions on Control Systems Technology, 21(3), 842–851. 10.1109/tcst.2012.2186634 DOI: 10.1109/tcst.2012.2186634
Tang, B. P., Song, T., Li, F., & Deng, L. (2014). Fault diagnosis for a wind turbine transmission system based on manifold learning and Shannon wavelet support vector machine. Renewable Energy, 62, 1–9. 10.1016/j.renene.2013.06.025 DOI: 10.1016/j.renene.2013.06.025
Tanveer, M., Rajani, T., Rastogi, R., Shao, Y. H., & Ganaie, M. A. (2022). Comprehensive review on twin support vector machines. Annals of Operations Research. 10.1007/s10479-022-04575-w DOI: 10.1007/s10479-022-04575-w
Tanveer, M., Sharma, S., Rastogi, R., & Anand, P. (2021). Sparse support vector machine with pinball loss. Transactions on Emerging Telecommunications Technologies, 32(2), 13. 10.1002/ett.3820 DOI: 10.1002/ett.3820
Tao, F., Qi, Q. L., Liu, A., & Kusiak, A. (2018). Data-driven smart manufacturing. Journal of Manufacturing Systems, 48, 157–169. 10.1016/j.jmsy.2018.01.006 DOI: 10.1016/j.jmsy.2018.01.006
Tao, F., Qi, Q. L., Wang, L. H., & Nee, A. Y. C. (2019). Digital twins and cyber-physical systems toward smart manufacturing and industry 4.0: correlation and comparison. Engineering, 5(4), 653–661. 10.1016/j.eng.2019.01.014 DOI: 10.1016/j.eng.2019.01.014
Tao, G. (2014). Direct adaptive actuator failure compensation control: A tutorial. Journal of Control and Decision, 1(1), 75–101. 10.1080/23307706.2014.885292 DOI: 10.1080/23307706.2014.885292
Tao, X., Zhang, D. P., Wang, Z. H., Liu, X. L., Zhang, H. Y., & Xu, D. (2020). Detection of power line insulator defects using aerial images analyzed with convolutional neural networks. IEEE Transactions on Systems Man Cybernetics-Systems, 50(4), 1486–1498. 10.1109/tsmc.2018.2871750 DOI: 10.1109/tsmc.2018.2871750
Taqvi, S. A. A., Zabiri, H., Tufa, L. D., Uddin, F., Fatima, S. A., & Maulud, A. S. (2021). A review on data-driven learning approaches for fault detection and diagnosis in chemical processes. Chembioeng Reviews, 8(3), 239–259. 10.1002/cben.202000027 DOI: 10.1002/cben.202000027
Tidriri, K., Chatti, N., Verron, S., & Tiplica, T. (2016). Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges. Annual Reviews in Control, 42, 63–81. 10.1016/j.arcontrol.2016.09.008 DOI: 10.1016/j.arcontrol.2016.09.008
Tolouei, H., & Shoorehdeli, M. A. (2017). Nonlinear parity approach to sensor fault detection in pH neutralization system [Conference Paper]. 2017 25th Iranian conference on electrical engineering (ICEE). https://doi.org/10.1109/IranianCEE.2017.7985165
Tornil-Sin, S., Ocampo-Martinez, C., Puig, V., & Escobet, T. (2012). Robust fault detection of non-linear systems using set-membership state estimation based on constraint satisfaction. Engineering Applications of Artificial Intelligence, 25(1), 1–10. 10.1016/j.engappai.2011.07.007 DOI: 10.1016/j.engappai.2011.07.007
Tran, M. K., & Fowler, M. (2020). Sensor fault detection and isolation for degrading lithium-ion batteries in electric vehicles using parameter estimation with recursive least squares. Batteries-Basel, 6(1), 16. 10.3390/batteries6010001 DOI: 10.3390/batteries6010001
Ugwiri, M. A., Paciello, V., Lay-Ekuakille, A., Pietrosanto, A., & Liguori, C. (2022). Parameter estimation for fault detection and classification in centrifugal pumps [Conference Paper]. 2022 IEEE International Instrumentation and Measurement Technology Conference, Ottawa, CANADA. https://doi.org/10.1109/I2MTC48687.2022.9806623
van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. 10.1007/s11192-009-0146-3 DOI: 10.1007/s11192-009-0146-3
Venkatasubramanian, V., Rengaswamy, R., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis Part II: Quantitative model and search strategies. Computers & Chemical Engineering, 27(3), 313–326. 10.1016/s00981354(02)00161-8 DOI: 10.1016/s00981354(02)00161-8
Wang, B., Jahanshahi, H., Volos, C., Bekiros, S., Khan, M. A., Agarwal, P., & Aly, A. A. (2021). A new RBF neural network-based fault-tolerant active control for fractional time-delayed systems. Electronics, 10(12), 17. 10.3390/electronics10121501 DOI: 10.3390/electronics10121501
Wang, B., Shen, Y. Y., & Zhang, Y. M. (2020). Active fault-tolerant control for a quadrotor helicopter against actuator faults and model uncertainties. Aerospace Science and Technology, 99, 15. 10.1016/j.ast.2020.105745 DOI: 10.1016/j.ast.2020.105745
Wang, H., Xu, J. W., Yan, R. Q., & Gao, R. X. (2020b). A New intelligent bearing fault diagnosis method using sdp representation and SE-CNN. IEEE Transactions on Instrumentation and Measurement, 69(5), 2377–2389. 10.1109/tim.2019.2956332 DOI: 10.1109/tim.2019.2956332
Wang, J. J., Ma, Y. L., Zhang, L. B., Gao, R. X., & Wu, D. Z. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144–156. 10.1016/j.jmsy.2018.01.003 DOI: 10.1016/j.jmsy.2018.01.003
Wang, M. H., Lu, S. D., & Liao, R. M. (2022). Fault diagnosis for power cables based on convolutional neural network with chaotic system and discrete wavelet transform. IEEE Transactions on Power Delivery, 37(1), 582–590. 10.1109/tpwrd.2021.3065342 DOI: 10.1109/tpwrd.2021.3065342
Wang, R. R., & Wang, J. M. (2013). Passive actuator fault-tolerant control for a class of overactuated nonlinear systems and applications to electric vehicles. IEEE Transactions on Vehicular Technology, 62(3), 972–985. 10.1109/tvt.2012.2232687 DOI: 10.1109/tvt.2012.2232687
Wang, T., Chen, M., Song, X., & Shen, H. (2019). RETRACTION: Robust fault -tolerant mixed passive and H infinity control for uncertain systems and its application in a semi -active suspension system [Retraction]. Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering, 233(5): NP9-NP9. https://doi.org/10.1177/0959651818759847
Wang, X. (2022). Research on Network Fault Diagnosis Based on Fault Tree Analysis [Conference Paper]. IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers. Dalian, China.
Wang, X., Mao, D. X., & Li, X. D. (2021). Bearing fault diagnosis based on vibro-acoustic data fusion and 1D-CNN network. Measurement, 173, 13. 10.1016/j.measurement.2020.108518 DOI: 10.1016/j.measurement.2020.108518
Wang, Y. (2018). Research on the Fault Diagnosis of Mechanical Equipment Vibration System Based on Expert System [Proceedings Paper]. Proceeding of the 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPCS), 636–641. https://doi.org/10.1109/sdpc.2018.00123
Widodo, A., & Yang, B. S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21(6), 2560–2574. 10.1016/j.ymssp.2006.12.007 DOI: 10.1016/j.ymssp.2006.12.007
Wu, Y. K., Jiang, B., Lu, N. Y., Yang, H., & Zhou, Y. (2017). Multiple incipient sensor faults diagnosis with application to high-speed railway traction devices. ISA Transactions, 67, 183–192. 10.1016/j.isatra.2016.12.001 DOI: 10.1016/j.isatra.2016.12.001
Wu, Y. T., Zhao, D., Liu, S., & Li, Y. Y. (2022). Fault detection for linear discrete time-varying systems with multiplicative noise based on parity space method. ISA Transactions, 121, 156–170. 10.1016/j.isatra.2021.04.018 DOI: 10.1016/j.isatra.2021.04.018
Xu, F., Tan, J. B., Wang, X. Q., Puig, V., Liang, B., Yuan, B., & Liu, H. D. (2017). Generalized set-theoretic unknown input observer for LPV systems with application to state estimation and robust fault detection. International Journal of Robust and Nonlinear Control, 27(17), 3812–3832. 10.1002/rnc.3773 DOI: 10.1002/rnc.3773
Xu, P., Liu, J. C., Shang, L. L., & Zhang, W. L. (2022). Industrial process fault detection and diagnosis framework based on enhanced supervised kernel entropy component analysis. Measurement, 196, 15. 10.1016/j.measurement.2022.111181 DOI: 10.1016/j.measurement.2022.111181
Xu, X. J., Yan, X. P., Sheng, C. X., Yuan, C. Q., Xu, D. L., & Yang, J. B. (2020). A belief rule-based expert system for fault diagnosis of marine diesel engines. IEEE Transactions on Systems Man Cybernetics-Systems, 50(2), 656–672. 10.1109/tsmc.2017.2759026 DOI: 10.1109/tsmc.2017.2759026
Yan, J. H., Meng, Y., Lu, L., & Li, L. (2017). Industrial big data in an industry 4.0 environment: Challenges, schemes, and applications for predictive maintenance [Article]. IEEE Access, 5, 23484–23491. 10.1109/access.2017.2765544 DOI: 10.1109/access.2017.2765544
Yan, W., Wang, J., Lu, S., Zhou, M., & Peng, X. (2023). A review of real-time fault diagnosis methods for industrial smart manufacturing. Processes, 11(2), 369. DOI: 10.3390/pr11020369
Yang, H. Y., Jiang, Y. C., & Yin, S. (2021). Adaptive fuzzy fault-tolerant control for markov jump systems with additive and multiplicative actuator faults. IEEE Transactions on Fuzzy Systems, 29(4), 772–785. 10.1109/tfuzz.2020.2965884 DOI: 10.1109/tfuzz.2020.2965884
Ye, Y. H., Zhang, Y. F., Chen, Y., Han, X. S., & Jiang, F. (2020). Cellulose nanofibrils enhanced, strong, stretchable, freezing-tolerant ionic conductive organohydrogel for multi-functional sensors. Advanced Functional Materials, 30(35), 12. 10.1002/adfm.202003430 DOI: 10.1002/adfm.202003430
Yin, S., Ding, S. X., Xie, X. C., & Luo, H. (2014). A review on basic data-driven approaches for industrial process monitoring. IEEE Transactions on Industrial Electronics, 61(11), 6418–6428. 10.1109/tie.2014.2301773 DOI: 10.1109/tie.2014.2301773
Yu, W. J., Dillon, T., Mostafa, F., Rahayu, W., & Liu, Y. H. (2020). A global manufacturing big data ecosystem for fault detection in predictive maintenance. IEEE Transactions on Industrial Informatics, 16(1), 183–192. 10.1109/tii.2019.2915846 DOI: 10.1109/tii.2019.2915846
Yu, Z. Q., Zhang, Y. M., Jiang, B., Fu, J., & Jin, Y. (2022). A review on fault-tolerant cooperative control of multiple unmanned aerial vehicles [Review]. Chinese Journal of Aeronautics, 35(1), 1–18. 10.1016/j.cja.2021.04.022 DOI: 10.1016/j.cja.2021.04.022
Zakaria, F., Johari, D., Musirin, I., & Ieee. (2012). Artificial Neural Network (ANN) Application in Dissolved Gas Analysis (DGA) Methods for the Detection of Incipient Faults in Oil-Filled Power Transformer [Proceedings Paper]. Proceeding of the 2012 IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2012). https://doi.org/10.1109/ICCSCE.2012.6487165
Zhakov, A., Zhu, H. L., Siegel, A., Rank, S., Schmidt, T., Fienhold, L., & Hummel, S. (2020). Application of ANN for fault detection in overhead transport systems for semiconductor fab. IEEE Transactions on Semiconductor Manufacturing, 33(3), 337–345. 10.1109/tsm.2020.2984326 DOI: 10.1109/tsm.2020.2984326
Zhang, G., Hu, D. Y., & Zhang, T. Q. (2019). Stochastic resonance in unsaturated piecewise nonlinear bistable system under multiplicative and additive noise for bearing fault diagnosis. IEEE Access, 7, 58435–58448. 10.1109/access.2019.2914138 DOI: 10.1109/access.2019.2914138
Zhang, Q. H., & Basseville, M. (2014). Statistical detection and isolation of additive faults in linear time-varying systems. Automatica, 50(10), 2527–2538. 10.1016/j.automatica.2014.09.004 DOI: 10.1016/j.automatica.2014.09.004
Zhang, W., Li, X., Ma, H., & Luo, Z. (2021). Transfer learning using deep representation regularization in remaining useful life prediction across operating conditions. Reliability Engineering & System Safety., 211, 13. 10.1016/j.ress.2021.107556 DOI: 10.1016/j.ress.2021.107556
Zhang, W., Wang, Z. W., & Li, X. (2023). Blockchain-based decentralized federated transfer learning methodology for collaborative machinery fault diagnosis. Reliability Engineering & System Safety, 229, 12. 10.1016/j.ress.2022.108885 DOI: 10.1016/j.ress.2022.108885
Zhang, X., Wang, H. F., Wu, B., Zhou, Q., & Hu, Y. M. (2023b). A novel data-driven method based on sample reliability assessment and improved CNN for machinery fault diagnosis with non-ideal data. Journal of Intelligent Manufacturing, 34(5), 2449–2462. 10.1007/s10845-022-01944-x DOI: 10.1007/s10845-022-01944-x
Zhao, B., Zhang, X. M., Li, H., & Yang, Z. B. (2020). Intelligent fault diagnosis of rolling bearings based on normalized CNN considering data imbalance and variable working conditions. Knowledge-Based Systems, 199, 16. 10.1016/j.knosys.2020.105971 DOI: 10.1016/j.knosys.2020.105971
Zhao, Y., & Shen, Y. (2019). Distributed event-triggered state estimation and fault detection of nonlinear stochastic systems. Journal of the Franklin Institute-Engineering and Applied Mathematics, 356(17), 10335–10354. 10.1016/j.jfranklin.2018.04.027 DOI: 10.1016/j.jfranklin.2018.04.027
Zhao, Z. G., Huang, Q. Q., & Jiang, S. W. (2022). Fault-tolerant control of clutch actuator motor in six-speed dry dual clutch transmission downshift process. Mechatronics, 84, 13. 10.1016/j.mechatronics.2022.102791 DOI: 10.1016/j.mechatronics.2022.102791
Zheng, J. D., Pan, H. Y., & Cheng, J. S. (2017). Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mechanical Systems and Signal Processing, 85, 746–759. 10.1016/j.ymssp.2016.09.010 DOI: 10.1016/j.ymssp.2016.09.010
Zheng, P., Wang, H. H., Sang, Z. Q., Zhong, R. Y., Liu, Y. K., Liu, C., Mubarok, K., Yu, S. Q., & Xu, X. (2018). Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives. Frontiers of Mechanical Engineering, 13(2), 137–150. 10.1007/s11465-018-0499-5 DOI: 10.1007/s11465-018-0499-5
Zhong, M., Xue, T., Song, Y., Ding, S. X., & Ding, E. L. (2021). Parity space vector machine approach to robust fault detection for linear discrete-time systems. IEEE Transactions on Systems Man Cybernetics-Systems, 51(7), 4251–4261. 10.1109/tsmc.2019.2930805 DOI: 10.1109/tsmc.2019.2930805
Zhong, M. Y., Du, X. T., Song, Y., Xue, T., & Ding, S. X. (2022). Event-triggered parity space approach to fault detection for linear discrete-time systems. IEEE Transactions on Systems Man Cybernetics-Systems, 52(8), 4813–4822. 10.1109/tsmc.2021.3103816 DOI: 10.1109/tsmc.2021.3103816
Zhong, M. Y., Song, Y., & Ding, S. X. (2015). Parity space-based fault detection for linear discrete time-varying systems with unknown input. Automatica, 59, 120–126. 10.1016/j.automatica.2015.06.013 DOI: 10.1016/j.automatica.2015.06.013
Zhong, M. Y., Song, Y., Xue, T., Yang, R., & Li, W. B. (2018). Parity Space-Based Fault Detection by Minimum Error Minimax Probability Machine [Proceedings Paper]. Proceeding of the 2018 International-Federation of Automatic Control (IFAC) Papersonline, 51(24), 1292–1297. https://doi.org/10.1016/j.ifacol.2018.09.568
Zhong, S. S., Fu, S., & Lin, L. (2019). A novel gas turbine fault diagnosis method based on transfer learning with CNN. Measurement, 137, 435–453. 10.1016/j.measurement.2019.01.022 DOI: 10.1016/j.measurement.2019.01.022
Zhou, D. H., Ji, H. Q., He, X., & Shang, J. (2018). Fault detection and isolation of the brake cylinder system for electric multiple units. IEEE Transactions on Control Systems Technology, 26(5), 1744–1757. 10.1109/tcst.2017.2718979 DOI: 10.1109/tcst.2017.2718979
Zhou, H. X., Huang, X., Wen, G. R., Lei, Z. H., Dong, S. Z., Zhang, P., & Chen, X. F. (2022). Construction of health indicators for condition monitoring of rotating machinery: A review of the research. Expert Systems with Applications, 203, 24. 10.1016/j.eswa.2022.117297 DOI: 10.1016/j.eswa.2022.117297
Zhou, X. F., Jiang, W. H., Tian, Y. J., & Shi, Y. (2010). Kernel subclass convex hull sample selection method for SVM on face recognition. Neurocomputing, 73(10–12), 2234–2246. 10.1016/j.neucom.2010.01.008 DOI: 10.1016/j.neucom.2010.01.008
Zhu, X. D., Xia, Y. Q., & Fu, M. Y. (2020). Fault estimation and active fault-tolerant control for discrete-time systems in finite-frequency domain]. ISA Transactions, 104, 184–191. 10.1016/j.isatra.2019.02.014 DOI: 10.1016/j.isatra.2019.02.014
Zonta, T., da Costa, C. A., Righi, R. D., de Lima, M. J., da Trindade, E. S., & Li, G. P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering, 150, 17. 10.1016/j.cie.2020.106889 DOI: 10.1016/j.cie.2020.106889
