A review of intelligent methods of health assessment technology

Abstract

The core technology of prognostics and health management, a key technology that detects system anomalies, is health assessment, which analyzes and diagnoses the current system working status and quantitatively assesses the health of the system. This paper reviews the development of health assessment technology in recent years from three aspects: health definition, health assessment indicators, and health assessment approaches. In terms of health definition, this paper summarizes three common definition methods. Health assessment indicators are reviewed from four levels: process variables, data features, residuals, and fusion indicators. Finally, health assessment approaches are divided into model-based, data-driven, and fusion approaches. Concerning the data-driven approach, rapidly developing health assessment research based on an intelligent approach is discussed. The paper also compares various approaches and identifies the current challenges and development prospects of this technology.

Keywords

Prognostics and health management / health assessment / intelligent methods / industrial systems

References

[1] Sheppard JW,Wilmer TJ.IEEE standards for prognostics and health management.IEEE Aerosp Electron Syst Mag2009;24:34-41.

[2] Si X,Hu C.Remaining useful life estimation-a review on the statistical data driven approaches.Eur J Oper Res2011;213:1-14.

[3] Liao L.Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction.IEEE Trans Rel2014;63:191-207

[4] Dai J,Pecht M.Prognostics-based risk mitigation for telecom equipment under free air cooling conditions.Appl Energ2012;99:423-9.

[5] Lee J,Zhao W,Liao L.Prognostics and health management design for rotary machinery systems-reviews, methodology and applications.Mech Syst Signal Pr2014;42:314-34.

[6] Ahmad R.An overview of time-based and condition-based maintenance in industrial application.Comput Ind Eng2012;63:135-49.

[7] Vichare NM.Prognostics and health management of electronics.IEEE Trans Comp Packag Technol2006;29:222-9

[8] Zio E.Some challenges and opportunities in reliability engineering.IEEE Trans Rel2016;65:1769-82

[9] Zhao Z,Cai KY.A profust reliability based approach to prognostics and health management.IEEE Trans Rel2014;63:26-41

[10] Luo XL,Wang HX.Status and development of research on UAV fault prediction and health management.Computer Measurement and Control2021;29:1-5. (in Chinese)

[11] Wang F,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]

[12] Nguyen VD,Yang KF et al.A review: prognostics and health management in automotive and aerospace.Int J Progn Health M2019;10:35

[13] Weiss BA.Measurement and evaluation for prognostics and health management (phm) for manufacturing operations-summary of an interactive workshop highlighting PHM Trends.Int J Progn Health Manag2021;12:online ahead of print PMCID:PMC8381745

[14] Zhou D.Fault diagnosis techniques for dynamic systems.Acta Automatica Sinica2009;35:748-58

[15] Venkatasubramanian V,Yin K.A review of process fault detection and diagnosis: part I: quantitative model-based methods.Comput Chem Eng2003;27:293-311

[16] Lu H, Kolarik WJ, Lu SS. Real-time performance reliability prediction.IEEE Trans Rel2001;50:353-7

[17] Xu Z, Ji Y, Zhou D. A new real-time reliability prediction method for dynamic systems based on on-line fault prediction.IEEE Trans Rel2009;58:523-38

[18] Xu Z, Ji Y, Zhou D. Real-time reliability prediction for a dynamic system based on the hidden degradation process identification.IEEE Trans Rel2008;57:230-42

[19] Lu S,Kolarik WJ.Multivariate performance reliability prediction in real-time.Reliab Eng Syst Safe2001;72:39-45

[20] Liao L.Discovering prognostic features using genetic programming in remaining useful life prediction.IEEE Trans Ind Electron2014;61:2464-72.

[21] Su X,Pecht M,Ye Z.Interacting multiple model particle filter for prognostics of lithium-ion batteries.Microelectron Reliab2017;70:59-69.

[22] Lin J.Remaining useful life prediction of lithium-ion battery based on auto-regression and particle filter.Int J Intell Comput Cybern2021;14:218-37

[23] Hu C,Jain G.Remaining useful life assessment of lithium-ion batteries in implantable medical devices.J Power Sources2018;375:118-30.

[24] Sun Y,Pecht M.Remaining useful life prediction for lithium-ion batteries based on an integrated health indicator.Microelectron Reliab2018;88-90:1189-94

[25] Shi J,Wu D.Battery health management using physics-informed machine learning: online degradation modeling and remaining useful life prediction.Mech Syst Signal Pr2022;179:109347

[26] Soualhi A,Clerc G.Prognosis of bearing failures using hidden markov models and the adaptive neuro-fuzzy inference system.IEEE Trans Ind Electron2014;61:2864-74

[27] Soualhi A,Zerhouni N.Bearing health monitoring based on Hilbert-Huang transform, support vector machine, and regression.IEEE Trans Instrum Meas2015;64:52-62

[28] Zhu K,Ye D.Online condition monitoring in micromilling: a force waveform shape analysis approach.IEEE Trans Ind Electron2015;62:3806-13.

[29] Frank PM.Survey of robust residual generation and evaluation methods in observer-based fault detection systems.J Process Contr1997;7:403-24.

[30] Piltan F.Crack size identification for bearings using an adaptive digital twin.Sensors2021;21:5009 PMCID:PMC8348473

[31] Wang L,Zhang W.Intelligent fault diagnosis algorithm for fiber optic current transformer.Int J Appl Electromagn Mech2020;64:3-10

[32] Zhang Y,Liu Z,Cheng L.Concurrent fault diagnosis of modular multilevel converter with Kalman filter and optimized support vector machine.Syst Sci Control Eng2019;7:43-53

[33] Yu M, Wang D. Model-Based health monitoring for a vehicle steering system with multiple faults of unknown types.IEEE Trans Ind Electron2014;61:3574-86

[34] Gao Z,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 Electron2015;62:3757-67

[35] Wang G.Data-driven fault-tolerant control design for wind turbines with robust residual generator.Control Theory A2015;9:1173-9

[36] Ge W,Zhou J,Jin Q.Incipient fault detection based on fault extraction and residual evaluation.Ind Eng Chem Res2015;54:3664-77.

[37] Svärd C,Frisk E.Data-driven and adaptive statistical residual evaluation for fault detection with an automotive application.Mech Syst Signal Pr2014;45:170-92

[38] Sahwee Z,Sahari KSM.Experimental evaluation of data fusion algorithm for residual generation in detecting uav servo actuator fault.Int J Micro Air Veh2015;7:133-45

[39] Javed K,Zerhouni N.Enabling health monitoring approach based on vibration data for accurate prognostics.IEEE Trans Ind Electron2015;62:647-56

[40] Joliffe IT.Principal component analysis and exploratory factor analysis.Stat Methods Med Res1992;1:69-95

[41] Bejaoui I,Xibilia MG.Remaining useful life prediction of broken rotor bar based on data-driven and degradation model.Appl Sci2021;11:7175.

[42] Li JL,Yang ZJ.Fault diagnosis of mine hoist braking system based on three layers information fusion.2018;38:408-12. (in Chinese)

[43] Zou YJ,Qiao JQ,Song JC.Bearing fault feature extraction of roller crusher motor based on time-frequency image.Journal of China Coal Society2018;43:623-33. (in Chinese)

[44] Guo Y.Fault diagnosis of VRF air-conditioning system based on improved Gaussian mixture model with PCA approach.Int J Refrig2020;118:1-11.

[45] Ahmad Z,Ahmad S,Kim JM.Multistage centrifugal pump fault diagnosis using informative ratio principal component analysis.Sensors2021;22:179 PMCID:PMC8747656

[46] Moghaddass R.An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process.Reliab Eng Syst Safe2014;124:92-104

[47] Zhao X.A new local-global deep neural network and its application in rotating machinery fault diagnosis.Neurocomputing2019;366:215-33.

[48] Shen Z,Chen X,Liu Z.A monotonic degradation assessment index of rolling bearings using fuzzy support vector data description and running time.Sensors2012;12:10109-35 PMCID:PMC3472819

[49] Ye Q.A multichannel data fusion method based on multiple deep belief networks for intelligent fault diagnosis of main reducer.Symmetry2020;12:483.

[50] Zheng C,Wang S.Fault diagnosis model and application of water injection well based on spc rules and real-time data fusion.J Phys: Conf Ser2021;2095:012091

[51] Li X,Wang W,Li D.Multiphysical field measurement and fusion for battery electric-thermal-contour performance analysis.Appl Energ2020;262:114518

[52] 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]

[53] Zhang Y.Detection and diagnosis of sensor and actuator failures using IMM estimator.IEEE Trans Aerosp Electron Syst1998;34:1293-313

[54] Zhao S,Liu F.Fault detection and diagnosis of multiple-model systems with mismodeled transition probabilities.IEEE Trans Ind Electron2015;62:5063-71.

[55] Compare M,Turati P.Interacting multiple-models, state augmented Particle Filtering for fault diagnostics.Probabilist Eng Mech2015;40:12-24.

[56] Ducard G.Efficient nonlinear actuator fault detection and isolation system for unmanned aerial vehicles.J Guid Control Dynam2008;31:225-37.

[57] Lu P,van Kampen E.Selective-reinitialization multiple-model adaptive estimation for fault detection and diagnosis.J Guid Control Dynam2015;38:1409-24

[58] Wang Y,Li R,Zhang X.Multi-fault diagnosis of interacting multiple model batteries based on low inertia noise reduction.IEEE Access2021;9:18465-80

[59] Zhou DH,Si XS.A survey on anomaly detection, life prediction and maintenance decision for industrial processes.Acta Automatica Sinica2013;39:711-22.

[60] Dolev E.Introduction to the special section on prognostics and health management.IEEE Trans Rel2009;58:262-3

[61] Ai YB,Zhang WD.Fault diagnosis of high-speed railway gearboxes based on performance degradation and material damage characterization.Control and Decision2018;33:1264-70. (in Chinese)

[62] Chen L,Xu X. 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]

[63] Yan SF,Zheng CS.Integrated transmission remaining life prediction based on competitive failure.Automotive Engineering2019;41:426-31 + 61. (in Chinese)

[64] Bei W,Gao P.Gear typical fault modeling and fault signal characteristics analysis.Forsch Ingenieurwes2022;86:735-50

[65] Li N,Lin J.An improved exponential model for predicting remaining useful life of rolling element bearings.IEEE Trans Ind Electron2015;62:7762-73

[66] Miao Q,Cui H,Pecht M.Remaining useful life prediction of lithium-ion battery with unscented particle filter technique.Microelectron Reliab2013;53:805-10

[67] Xing Y,Tsui KL.An ensemble model for predicting the remaining useful performance of lithium-ion batteries.Microelectron Reliab2013;53:811-20.

[68] Ruan YL.Research on optimization of hierarchical energy management strategy for fuel cell vehicle.Electronic Measurement Technology2021;44:1-7. (in Chinese)

[69] Jia CJ,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]

[70] Zhao Z,Cai KY.A health evaluation method of multicopters modeled by stochastic hybrid system.Aerosp Sci Technol2017;68:149-62.

[71] Zhao Z,Wang X,Wang L.Reliable flight performance assessment of multirotor based on interacting multiple model particle filter and health degree.Chinese J Aeronaut2019;32:444-53

[72] Venkatasubramanian V,Kavuri SN.A review of process fault detection and diagnosis: part II: qualitative models and search strategies.Comput Chem Eng2003;27:313-26.

[73] Guo YT. 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]

[74] Mo HB.Fault diagnosis based on interval analytic redundancy relation.Journal of nanjing university of aeronautics and astronautics2021;53:972-80. (in Chinese)

[75] 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]

[76] Lu XJ.Application of the P-box theory and HGWO-SVM in the fault diagnosis of rolling bearings.Journal of Vibration and Shock2021;40:234-41. (in Chinese)

[77] Li LF.Discrete-time 2-order sliding mode fault-tolerant tracking control for non-gaussian nonlinear stochastic distribution control systems with missing measurements.Complexity2020;2020:1-13

[78] Pang X,Ye L.Vibration reliability evaluation of main fan spindle bearings.Shock and Vibration2019;2019:1-12

[79] He J,Jiang Y,An L.Science and Technology on Underwater Vehicle Laboratory Harbin Engineering UniversityHarbin 150001ChinaPropeller fault diagnosis based on a rank particle filter for autonomous underwater vehicles.Brod2018;69:147-64

[80] Li X,Wan F,Zhang G.Condition-based maintenance strategy optimization of meta-action unit considering imperfect preventive maintenance based on Wiener process.Flex Serv Manuf J2022;34:204-33

[81] Lien Nguyen TB,Ananou B,Pinaton J.Fault prognosis for batch production based on percentile measure and gamma process: Application to semiconductor manufacturing.J Process Contr2016;48:72-80.

[82] Guo LY,Dai YY.Time-dependent failure probability of corroded pipelines based on different stochastic degradation processes.Acta Petrolei Sinica2019;40:1542-52. (in Chinese)

[83] Zuo L,Zhang ZH,Liu Y.A spiking neural network-based approach to bearing fault diagnosis.J Manuf Syst2021;61:714-24.

[84] Li J,Li Q.Intelligent fault diagnosis of rolling bearings under imbalanced data conditions using attention-based deep learning method.Measurement2022;189:110500.

[85] Ai S,Cai G.A real-time fault diagnosis method for hypersonic air vehicle with sensor fault based on the auto temporal convolutional network.Aerosp Sci Technol2021;119:107220

[86] Pu YB,Zhao FC,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]

[87] Jin XH,Shan JH. 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]

[88] Liu J,Sun YH,Ji HP.Fault diagnosis method for equipment driven by knowledge and data fusion.Journal of Zhengzhou University2022;54:39-46. (in Chinese)

[89] 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]