REFERENCES

1. Sheppard JW, Kaufman MA, Wilmer TJ. IEEE standards for prognostics and health management. IEEE Aerosp Electron Syst Mag 2009;24:34-41.

2. Si X, Wang W, Hu C, Zhou D. Remaining useful life estimation-a review on the statistical data driven approaches. Eur J Oper Res 2011;213:1-14.

3. Chen X, Yu J, Tang D, Wang Y. Remaining useful life prognostic estimation for aircraft subsystems or components: a review. IEEE 2011 10th International Conference on Electronic Measurement & Instruments; 2011. p. 94-8.

4. Liao L, Kottig F. Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction. IEEE Trans Rel 2014;63:191-207.

5. Dai J, Das D, Pecht M. Prognostics-based risk mitigation for telecom equipment under free air cooling conditions. Appl Energ 2012;99:423-9.

6. Lee J, Wu F, Zhao W, Ghaffari M, Liao L, Siegel D. Prognostics and health management design for rotary machinery systems-reviews, methodology and applications. Mech Syst Signal Pr 2014;42:314-34.

7. Ahmad R, Kamaruddin S. An overview of time-based and condition-based maintenance in industrial application. Comput Ind Eng 2012;63:135-49.

8. Vichare NM, Pecht MG. Prognostics and health management of electronics. IEEE Trans Comp Packag Technol 2006;29:222-9.

9. Aaseng G, Patterson-Hine A, Garcia-Galan C. A review of system health state determination methods. 1st space exploration conference: continuing the voyage of discovery. Orlando: American Institute of Aeronautics and Astronautics; 2005. pp. 1-13.

10. Zio E. Some challenges and opportunities in reliability engineering. IEEE Trans Rel 2016;65:1769-82.

11. Zhao Z, Quan Q, Cai KY. A profust reliability based approach to prognostics and health management. IEEE Trans Rel 2014;63:26-41.

12. Luo XL, Tu L, Wang HX, et al. Status and development of research on UAV fault prediction and health management. Computer Measurement and Control 2021;29:1-5. (in Chinese).

13. Wang F, Jin JH, Chen YH. A review of research and application of prediction and health management technologies. "The 4th underwater unmanned systems technology summit"-manned/unmanned cooperative technology. Xi'an, Shaanxi, China; 2021:73-9. (in Chinese) Available from: https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C467SBiOvrai6TdxYiSzCnOET0Xr_I8pgMuCFSD7JyYj-v5wUJbUKgfxj9RIFk4eUCLR0MYVTOzSP7bClILT1jlHDcmD9X055C0%3d&uniplatform=NZKPT [Last accessed on 27 Jul 2023].

14. Nguyen VD, Kefalas M, Yang KF et al. A review: prognostics and health management in automotive and aerospace. Int J Progn Health M 2019;10:35.

15. Weiss BA, Brundage MP. Measurement and evaluation for prognostics and health management (phm) for manufacturing operations-summary of an interactive workshop highlighting PHM Trends. Int J Progn Health Manag 2021;12:online ahead of print.

16. Zhou D, Hu Y. Fault diagnosis techniques for dynamic systems. Acta Automatica Sinica 2009;35:748-58.

17. Venkatasubramanian V, Rengaswamy R, Yin K, Kavuri SN. A review of process fault detection and diagnosis: part I: quantitative model-based methods. Comput Chem Eng 2003;27:293-311.

18. Lu H, Kolarik WJ, Lu SS. Real-time performance reliability prediction. IEEE Trans Rel 2001;50:353-7.

19. Xu Z, Ji Y, Zhou D. A new real-time reliability prediction method for dynamic systems based on on-line fault prediction. IEEE Trans Rel 2009;58:523-38.

20. Xu Z, Ji Y, Zhou D. Real-time reliability prediction for a dynamic system based on the hidden degradation process identification. IEEE Trans Rel 2008;57:230-42.

21. Lu S, Lu H, Kolarik WJ. Multivariate performance reliability prediction in real-time. Reliab Eng Syst Safe 2001;72:39-45.

22. Liao L. Discovering prognostic features using genetic programming in remaining useful life prediction. IEEE Trans Ind Electron 2014;61:2464-72.

23. Su X, Wang S, Pecht M, Zhao L, Ye Z. Interacting multiple model particle filter for prognostics of lithium-ion batteries. Microelectron Reliab 2017;70:59-69.

24. Lin J, Wei M. Remaining useful life prediction of lithium-ion battery based on auto-regression and particle filter. Int J Intell Comput Cybern 2021;14:218-37.

25. Hu C, Ye H, Jain G, Schmidt C. Remaining useful life assessment of lithium-ion batteries in implantable medical devices. J Power Sources 2018;375:118-30.

26. Sun Y, Hao X, Pecht M, Zhou Y. Remaining useful life prediction for lithium-ion batteries based on an integrated health indicator. Microelectron Reliab 2018;88-90:1189-94.

27. Shi J, Rivera A, Wu D. Battery health management using physics-informed machine learning: online degradation modeling and remaining useful life prediction. Mech Syst Signal Pr 2022;179:109347.

28. Tian D, Deng J, Zio E, Maio F, Liao F. Failure modes detection of nuclear systems using machine learning. 2018 5th International Conference on Dependable Systems and Their Applications (DSA). Dalian: IEEE; 2018. pp. 35-43.

29. Aggogeri F, Pellegrini N, Taesi C, Tiboni M. Design for reliability of robotic systems based on the prognostic approach. 23rd International Conference on Mechatronics Technology (ICMT). Salerno: IEEE; 2019. pp. 1-5.

30. Sun Y, Han X. Research on vibration fault diagnosis technology of steam turbine unit in power plant based on wavelet theory. 3rd International Conference on Air Pollution and Environmental Engineering. Iop Publishing Ltd; 2020. pp. 012096.

31. Tang WZ, Fei CW, Bai GC, Ma S. Reliability quantitative analysis for rotor vibration based on WCFSE. 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE). Chengdu, China; 2013, pp. 28-31.

32. Boukra T, Lebaroud A. Identifying new prognostic features for remaining useful life prediction. 16th International Power Electronics and Motion Control Conference and Exposition (PEMC). Antalya: IEEE; 2014, pp. 1216-21.5.

33. Soualhi A, Razik H, Clerc G, Doan DD. Prognosis of bearing failures using hidden markov models and the adaptive neuro-fuzzy inference system. IEEE Trans Ind Electron 2014;61:2864-74.

34. Soualhi A, Medjaher K, Zerhouni N. Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression. IEEE Trans Instrum Meas 2015;64:52-62.

35. Zhu K, Mei T, Ye D. Online condition monitoring in micromilling: a force waveform shape analysis approach. IEEE Trans Ind Electron 2015;62:3806-13.

36. Frank PM, Ding X. Survey of robust residual generation and evaluation methods in observer-based fault detection systems. J Process Contr 1997;7:403-24.

37. Piltan F, Kim JM. Crack size identification for bearings using an adaptive digital twin. Sensors 2021;21:5009.

38. Dey S, Moura SJ. Robust fault diagnosis of uncertain one-dimensional wave equations. 2018 IEEE Conference on Decision and Control (CDC). Miami, FL, USA; 2018, pp. 2902-7.

39. Wang L, Zhao K, Zhang W, et al. Intelligent fault diagnosis algorithm for fiber optic current transformer. Int J Appl Electromagn Mech 2020;64:3-10.

40. Zhang Y, Hu H, Liu Z, Zhao M, Cheng L. Concurrent fault diagnosis of modular multilevel converter with Kalman filter and optimized support vector machine. Syst Sci Control Eng 2019;7:43-53.

41. Yu M, Wang D. Model-Based health monitoring for a vehicle steering system with multiple faults of unknown types. IEEE Trans Ind Electron 2014;61:3574-86.

42. Gao Z, Cecati C, Ding SX. A survey of fault diagnosis and fault-tolerant techniques-part I: fault diagnosis with model-based and signal-based approaches. IEEE Trans Ind Electron 2015;62:3757-67.

43. Wang G, Huang Z. Data-driven fault-tolerant control design for wind turbines with robust residual generator. Control Theory A 2015;9:1173-9.

44. Ge W, Wang J, Zhou J, Wu H, Jin Q. Incipient fault detection based on fault extraction and residual evaluation. Ind Eng Chem Res 2015;54:3664-77.

45. Svärd C, Nyberg M, Frisk E, Krysander M. Data-driven and adaptive statistical residual evaluation for fault detection with an automotive application. Mech Syst Signal Pr 2014;45:170-92.

46. Sahwee Z, Rahman NA, Sahari KSM. Experimental evaluation of data fusion algorithm for residual generation in detecting uav servo actuator fault. Int J Micro Air Veh 2015;7:133-45.

47. Javed K, Gouriveau R, Zerhouni N, Nectoux P. Enabling health monitoring approach based on vibration data for accurate prognostics. IEEE Trans Ind Electron 2015;62:647-56.

48. Joliffe IT, Morgan BJ. Principal component analysis and exploratory factor analysis. Stat Methods Med Res 1992;1:69-95.

49. Bejaoui I, Bruneo D, Xibilia MG. Remaining useful life prediction of broken rotor bar based on data-driven and degradation model. Appl Sci 2021;11:7175.

50. Li JL, Wang J, Yang ZJ. Fault diagnosis of mine hoist braking system based on three layers information fusion. Journal of Vibration, Measurement & Diagnosis 2018;38:408-12. (in Chinese).

51. Zou YJ, Tian MQ, Qiao JQ, Ma B, Song JC, Zhang WJ. Bearing fault feature extraction of roller crusher motor based on time-frequency image. Journal of China Coal Society 2018;43:623-33. (in Chinese).

52. Guo Y, Chen H. Fault diagnosis of VRF air-conditioning system based on improved Gaussian mixture model with PCA approach. Int J Refrig 2020;118:1-11.

53. Ahmad Z, Nguyen TK, Ahmad S, Nguyen CD, Kim JM. Multistage centrifugal pump fault diagnosis using informative ratio principal component analysis. Sensors 2021;22:179.

54. Moghaddass R, Zuo MJ. An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process. Reliab Eng Syst Safe 2014;124:92-104.

55. Zhao X, Jia M. A new local-global deep neural network and its application in rotating machinery fault diagnosis. Neurocomputing 2019;366:215-33.

56. Shen Z, He Z, Chen X, Sun C, Liu Z. A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time. Sensors 2012;12:10109-35.

57. Ye Q, Liu C. A multichannel data fusion method based on multiple deep belief networks for intelligent fault diagnosis of main reducer. Symmetry 2020;12:483.

58. Zheng C, Li A, Wang S, et al. Fault diagnosis model and application of water injection well based on spc rules and real-time data fusion. J Phys: Conf Ser 2021;2095:012091.

59. Li X, Zhang Z, Wang W, Tian Y, Li D, Tian J. Multiphysical field measurement and fusion for battery electric-thermal-contour performance analysis. Appl Energ 2020;262:114518.

60. Kumar S, Pecht M. Health monitoring of electronic products using symbolic time series analysis. AAAI fall symposium: artificial intelligence for prognostics; 2007. pp. 73-80. Available from: https://aaai.org/papers/0011-health-monitoring-of-electronic-products-using-symbolic-time-series-analysis/ [Last accessed on 28 Jul 2023].

61. Zhang Y, Li XR. Detection and diagnosis of sensor and actuator failures using IMM estimator. IEEE Trans Aerosp Electron Syst 1998;34:1293-313.

62. Zhao S, Huang B, Liu F. Fault detection and diagnosis of multiple-model systems with mismodeled transition probabilities. IEEE Trans Ind Electron 2015;62:5063-71.

63. Compare M, Baraldi P, Turati P, Zio E. Interacting multiple-models, state augmented Particle Filtering for fault diagnostics. Probabilist Eng Mech 2015;40:12-24.

64. Tudoroiu N, Sobhani-Tehrani E, Khorasani K. Interactive bank of unscented kalman filters for fault detection and isolation in reaction wheel actuators of satellite attitude control system. IECON 2006-32nd Annual Conference on IEEE Industrial Electronics; 2006. pp. 264-9.

65. Tudoroiu N, Khorasani K. Fault detection and diagnosis for reaction wheels of satellite's attitude control system using a bank of kalman filters. International Symposium on Signals, Circuits and Systems, 2005. ISSCS 2005; 2005, pp. 199-202.

66. Tudoroiu N, Khorasani K. Fault detection and diagnosis for Satellite's Attitude Control System (Acs) using an Interactive Multiple Model (Imm) approach. Proceedings of 2005 IEEE Conference on Control Applications, 2005. CCA 2005; 2005. pp. 1287-92.

67. Ducard G, Geering HP. Efficient nonlinear actuator fault detection and isolation system for unmanned aerial vehicles. J Guid Control Dynam 2008;31:225-37.

68. Lu P, Van Eykeren L, van Kampen E, Chu QP. Selective-reinitialization multiple-model adaptive estimation for fault detection and diagnosis. J Guid Control Dynam 2015;38:1409-24.

69. Wang Y, Meng D, Li R, Zhou Y, Zhang X. Multi-fault diagnosis of interacting multiple model batteries based on low inertia noise reduction. IEEE Access 2021;9:18465-80.

70. Zhou DH, Wei MH, Si XS. A survey on anomaly detection, life prediction and maintenance decision for industrial processes. Acta Automatica Sinica 2013;39:711-22.

71. Gu J, Pecht M. Prognostics and health management using physics-of-failure. 2008 Annual Reliability and Maintainability Symposium; 2008. pp. 481-7.

72. Dolev E. Introduction to the special section on prognostics and health management. IEEE Trans Rel 2009;58:262-3.

73. Ai YB, Sun C, Zhang WD. Fault diagnosis of high-speed railway gearboxes based on performance degradation and material damage characterization. Control and Decision 2018;33:1264-70. (in Chinese).

74. Chen L, Shan HQ, Xu X, Cui XL. Sensor fault diagnosis for ECAS systems based on extended Kalman filter banks. Journal of Vibration, Measurement and Diagnosis 2019;39:389-95 + 449.Available from: https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7iLik5jEcCI09uHa3oBxtWoN7AxdgDty5NEswongRr9QGE6E-7R8lj_4Nxwvez5bE3&uniplatform=NZKPT [Last accessed on 28 Jul 2023].

75. Yan SF, Ma B, Zheng CS. Integrated transmission remaining life prediction based on competitive failure. Automotive Engineering 2019;41:426-31 + 61. (in Chinese).

76. Bei W, Liu H, Gao P, Xiang C. Gear typical fault modeling and fault signal characteristics analysis. Forsch Ingenieurwes 2022;86:735-50.

77. Li N, Lei Y, Lin J, Ding SX. An improved exponential model for predicting remaining useful life of rolling element bearings. IEEE Trans Ind Electron 2015;62:7762-73.

78. Miao Q, Xie L, Cui H, Liang W, Pecht M. Remaining useful life prediction of lithium-ion battery with unscented particle filter technique. Microelectron Reliab 2013;53:805-10.

79. Xing Y, Ma EWM, Tsui KL, Pecht M. An ensemble model for predicting the remaining useful performance of lithium-ion batteries. Microelectron Reliab 2013;53:811-20.

80. Ruan YL, Zhan YD. Research on optimization of hierarchical energy management strategy for fuel cell vehicle. Electronic Measurement Technology 2021;44:1-7. (in Chinese).

81. Jia CJ, Zhu XP, Zhou Z. Detection and diagnosis of sensor and actuator failures for an UAV control system using IMM algorithm. Firepower and Command Control 2006:8-10 + 20. (in Chinese) Available from: https://hlyz.cbpt.cnki.net/WKD/WebPublication/paperDigest.aspx?paperID=a3b19ccd-ee52-4442-94b1-a0ee3081ce1e [Last accessed on 28 Jul 2023].

82. Zhao Z, Quan Q, Cai KY. A health evaluation method of multicopters modeled by stochastic hybrid system. Aerosp Sci Technol 2017;68:149-62.

83. Zhao Z, Yao P, Wang X, Xu J, Wang L, Yu J. Reliable flight performance assessment of multirotor based on interacting multiple model particle filter and health degree. Chinese J Aeronaut 2019;32:444-53.

84. Venkatasubramanian V, Rengaswamy R, Kavuri SN. A review of process fault detection and diagnosis: part II: qualitative models and search strategies. Comput Chem Eng 2003;27:313-26.

85. Guo YT, Deng XF. A diagnose method of analyzing redundancy obstacles based on bond graph model. Journal of Hunan Industry Polytechnic 2019;19:13-8. (in Chinese) Available from: https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7i8oRR1PAr7RxjuAJk4dHXon7g6VEuuff-X5QnkMbtMywzbGLyf-VJyr_rBi5wUANn&uniplatform=NZKPT [Last accessed on 28 Jul 2023].

86. Mo HB, Li YJ. Fault diagnosis based on interval analytic redundancy relation. Journal of nanjing university of aeronautics and astronautics 2021;53:972-80. (in Chinese).

87. Li H. Fault diagnosis and prediction of nonlinear electromechanical systems based on particle filtering and limit learning machine: Hefei University of Technology; 2020. (in Chinese) Available from: https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C475KOm_zrgu4lQARvep2SAkyRJRH-nhEQBuKg4okgcHYstLQy4siFAJYoWabuu4oBpJz3RdSAkPlYkqnTk-efYx&uniplatform=NZKPT [Last accessed on 28 Jul 2023].

88. Lu XJ, Shi CJ. Application of the P-box theory and HGWO-SVM in the fault diagnosis of rolling bearings. Journal of Vibration and Shock 2021;40:234-41. (in Chinese).

89. Li LF, Yao LN. Discrete-time 2-order sliding mode fault-tolerant tracking control for non-gaussian nonlinear stochastic distribution control systems with missing measurements. Complexity 2020;2020:1-13.

90. Pang X, Qiu M, Ye L, Chen L. Vibration reliability evaluation of main fan spindle bearings. Shock and Vibration 2019;2019:1-12.

91. He J, Li Y, Jiang Y, Li Y, An L. Science and Technology on Underwater Vehicle Laboratory Harbin Engineering University; Harbin 150001; China. Propeller fault diagnosis based on a rank particle filter for autonomous underwater vehicles. Brod 2018;69:147-64.

92. Li X, Ran Y, Wan F, Yu H, Zhang G, He Y. Condition-based maintenance strategy optimization of meta-action unit considering imperfect preventive maintenance based on Wiener process. Flex Serv Manuf J 2022;34:204-33.

93. Lien Nguyen TB, Djeziri M, Ananou B, Ouladsine M, Pinaton J. Fault prognosis for batch production based on percentile measure and gamma process: Application to semiconductor manufacturing. J Process Contr 2016;48:72-80.

94. Guo LY, Zhou J, Dai YY. Time-dependent failure probability of corroded pipelines based on different stochastic degradation processes. Acta Petrolei Sinica 2019;40:1542-52. (in Chinese).

95. Zuo L, Zhang L, Zhang ZH, Luo XL, Liu Y. A spiking neural network-based approach to bearing fault diagnosis. J Manuf Syst 2021;61:714-24.

96. Li J, Liu Y, Li Q. Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method. Measurement 2022;189:110500.

97. Ai S, Song J, Cai G. A real-time fault diagnosis method for hypersonic air vehicle with sensor fault based on the auto temporal convolutional network. Aerosp Sci Technol 2021;119:107220.

98. Pu YB, Wang YY, Zhao FC, Liu W, Luo TY. Design of fault prediction and early warning system for typical guided ammunition. Journal of Ordnance Equipment Engineering 2021;42:39-45. (in Chinese) Available from: http://www.cqvip.com/qk/90394a/20218/7105400416.html [Last accessed on 28 Jul 2023].

99. Jin XH, Sun Y, Shan JH, Wu GY. Fault diagnosis and prognosis for wind turbines: An overview. Chinese Journal of Scientific Instrument 2017;38:1041-53. (in Chinese) Available from: https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7iAEhECQAQ9aTiC5BjCgn0RoMgGxt8Zp43wUlxn_dMhBA9NKOThyovy8ETu2QO3D_w&uniplatform=NZKPT [Last accessed on 28 Jul 2023].

100. Liu J, Gao LC, Sun YH, Feng XZ, Ji HP. Fault diagnosis method for equipment driven by knowledge and data fusion. Journal of Zhengzhou University 2022;54:39-46. (in Chinese).

101. Wang YM. Fault diagnosis of diesel engine air management system based on fusion model. Science Technology and Engineering 2020;20:10280-6. (in Chinese) Available from: https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7iJTKGjg9uTdeTsOI_ra5_XbFvPVUqqVlHY6CvmpZStCsMIsP8nWOUkEz4blNJOOhC&uniplatform=NZKPT [Last accessed on 28 Jul 2023].