Diagnostics and prognostics for complex systems: A review of methods and challenges

[1]  Huajing Fang,et al.  A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery , 2017 .

[2]  Ning Lin,et al.  Multi-state reliability assessment for hydraulic lifting system based on the theory of dynamic Bayesian networks , 2016 .

[3]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[4]  Liang Gao,et al.  A New Deep Transfer Learning Based on Sparse Auto-Encoder for Fault Diagnosis , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[5]  Felician Campean,et al.  Automotive IVHM: Towards Intelligent Personalised Systems Healthcare , 2019, Proceedings of the Design Society: International Conference on Engineering Design.

[6]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part II: Fault Diagnosis With Knowledge-Based and Hybrid/Active Approaches , 2015, IEEE Transactions on Industrial Electronics.

[7]  M. S. Lebold,et al.  Hybrid reasoning for prognostic learning in CBM systems , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[8]  Minping Jia,et al.  Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions , 2020, Knowl. Based Syst..

[9]  J. Golinval,et al.  Fault detection based on Kernel Principal Component Analysis , 2010 .

[10]  Mohammad Pourgol-Mohammad,et al.  Design for Reliability of Complex System: Case Study of Horizontal Drilling Equipment with Limited Failure Data , 2014 .

[11]  Stefan Thurner,et al.  Complex systems: physics beyond physics , 2016, 1610.01002.

[12]  Michael Beetz,et al.  MatCALO: Knowledge-enabled machine learning in materials science , 2019, Computational Materials Science.

[13]  Lin Ma,et al.  Prognostic modelling options for remaining useful life estimation by industry , 2011 .

[14]  Venkat Venkatasubramanian,et al.  Prognostic and diagnostic monitoring of complex systems for product lifecycle management: Challenges and opportunities , 2005, Comput. Chem. Eng..

[15]  Bing Han,et al.  Data-driven based fault prognosis for industrial systems: a concise overview , 2020, IEEE/CAA Journal of Automatica Sinica.

[16]  Enrico Zio,et al.  Bayesian Network Modelling for the Wind Energy Industry: An Overview , 2020, Reliab. Eng. Syst. Saf..

[17]  Hesam Addin Arghand,et al.  Estimation of Remaining Useful Life of Rolling Element Bearings Using Wavelet Packet Decomposition and Artificial Neural Network , 2018, Iranian Journal of Science and Technology, Transactions of Electrical Engineering.

[18]  Katrina M. Groth,et al.  A Dynamic Bayesian Network Structure for Joint Diagnostics and Prognostics of Complex Engineering Systems , 2020, Algorithms.

[19]  S. Qin,et al.  Multimode process monitoring with Bayesian inference‐based finite Gaussian mixture models , 2008 .

[20]  Yaguo Lei,et al.  Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .

[21]  N. Zerhouni,et al.  Hidden Markov Models for failure diagnostic and prognostic , 2011, 2011 Prognostics and System Health Managment Confernece.

[22]  Felician Campean,et al.  Reliability Challenges for Automotive Aftertreatment Systems: a State-of-the-art Perspective , 2018 .

[23]  Hang Li,et al.  Fault Diagnosis and RUL Prediction of Nonlinear Mechatronic System via Adaptive Genetic Algorithm-Particle Filter , 2019, IEEE Access.

[24]  Beitong Zhou,et al.  Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network , 2019, Applied Energy.

[25]  George Nikolakopoulos,et al.  Principal Component Analysis of the start-up transient and Hidden Markov Modeling for broken rotor bar fault diagnosis in asynchronous machines , 2013, Expert Syst. Appl..

[26]  Ying Peng,et al.  Current status of machine prognostics in condition-based maintenance: a review , 2010 .

[27]  S.M. Rovnyak,et al.  Decision tree-based methodology for high impedance fault detection , 2004, IEEE Transactions on Power Delivery.

[28]  Alaa Mohamed Riad,et al.  Prognostics: a literature review , 2016, Complex & Intelligent Systems.

[29]  Satish T. S. Bukkapatnam,et al.  Dirichlet Process Gaussian Mixture Models for Real-Time Monitoring and Their Application to Chemical Mechanical Planarization , 2017, IEEE Transactions on Automation Science and Engineering.

[30]  Aitor Arnaiz,et al.  Application of Bayesian networks in prognostics for a new Integrated Vehicle Health Management concept , 2012, Expert Syst. Appl..

[31]  Chandrabhanu Malla,et al.  Rolling element bearing fault detection based on the complex Morlet wavelet transform and performance evaluation using artificial neural network and support vector machine , 2019, Noise & Vibration Worldwide.

[32]  Li Lin,et al.  Remaining useful life estimation of engineered systems using vanilla LSTM neural networks , 2018, Neurocomputing.

[33]  Mohammad Pourgol-Mohammad,et al.  Design for Reliability of Complex System with Limited Failure Data; Case Study of a Horizontal Drilling Equipment , 2014 .

[34]  Ying Xu,et al.  Fault detection of multimode non-Gaussian dynamic process using dynamic Bayesian independent component analysis , 2016, Neurocomputing.

[35]  Brian A. Weiss,et al.  A review of diagnostic and prognostic capabilities and best practices for manufacturing , 2019, J. Intell. Manuf..

[36]  Takehisa Yairi,et al.  A review on the application of deep learning in system health management , 2018, Mechanical Systems and Signal Processing.

[37]  Weihua Gui,et al.  A novel deep learning based fault diagnosis approach for chemical process with extended deep belief network. , 2019, ISA transactions.

[38]  Jong-Myon Kim,et al.  A reliable technique for remaining useful life estimation of rolling element bearings using dynamic regression models , 2018, Reliab. Eng. Syst. Saf..

[39]  Chao Liu,et al.  An adaptive spatiotemporal feature learning approach for fault diagnosis in complex systems , 2019, Mechanical Systems and Signal Processing.

[40]  Richard Foote,et al.  Mathematics and Complex Systems , 2007, Science.

[41]  Qinghua Zhang,et al.  Adaptive Kalman filter for actuator fault diagnosis , 2017, Autom..

[42]  Faisal Khan,et al.  Dynamic process fault detection and diagnosis based on a combined approach of hidden Markov and Bayesian network model , 2019, Chemical Engineering Science.

[43]  Shen Yin,et al.  Recursive Total Principle Component Regression Based Fault Detection and Its Application to Vehicular Cyber-Physical Systems , 2018, IEEE Transactions on Industrial Informatics.

[44]  Asoke K. Nandi,et al.  FAULT DETECTION USING SUPPORT VECTOR MACHINES AND ARTIFICIAL NEURAL NETWORKS, AUGMENTED BY GENETIC ALGORITHMS , 2002 .

[45]  Jinjiang Wang,et al.  Adaptive prognosis of centrifugal pump under variable operating conditions , 2019, Mechanical Systems and Signal Processing.

[46]  Guido Bugmann,et al.  NEURAL NETWORK DESIGN FOR ENGINEERING APPLICATIONS , 2001 .

[47]  Prashant Kumar,et al.  Review on Machine Learning Algorithm Based Fault Detection in Induction Motors , 2020, Archives of Computational Methods in Engineering.

[48]  Hong Chen,et al.  Data-Driven Design of Parity Space-Based FDI System for AMT Vehicles , 2015, IEEE/ASME Transactions on Mechatronics.

[49]  Shuhui Wang,et al.  Convolutional neural network-based hidden Markov models for rolling element bearing fault identification , 2017, Knowl. Based Syst..

[50]  Jinsong Yu,et al.  Remaining useful life prediction for lithium-ion batteries using a quantum particle swarm optimization-based particle filter , 2017 .

[51]  A. Majidian,et al.  Comparison of Fuzzy logic and Neural Network in life prediction of boiler tubes , 2007 .

[52]  Vladimir V. Mokshin,et al.  Adaptive genetic algorithms used to analyze behavior of complex system , 2019, Commun. Nonlinear Sci. Numer. Simul..

[53]  Biao Huang,et al.  Iterative Residual Generator for Fault Detection With Linear Time-Invariant State–Space Models , 2017, IEEE Transactions on Automatic Control.

[54]  Milton Borsato,et al.  An ontology-based model for prognostics and health management of machines , 2017, J. Ind. Inf. Integr..

[55]  Louise Travé-Massuyès,et al.  Bridging control and artificial intelligence theories for diagnosis: A survey , 2014, Eng. Appl. Artif. Intell..

[56]  Haidong Shao,et al.  Electric Locomotive Bearing Fault Diagnosis Using a Novel Convolutional Deep Belief Network , 2018, IEEE Transactions on Industrial Electronics.

[57]  Jeffrey Alun Jones,et al.  Comparison of Computational Prognostic Methods for Complex Systems Under Dynamic Regimes: A Review of Perspectives , 2019, Archives of Computational Methods in Engineering.

[58]  Robert X. Gao,et al.  Deep learning and its applications to machine health monitoring , 2019, Mechanical Systems and Signal Processing.

[59]  P. J. García Nieto,et al.  Hybrid PSO-SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability , 2015, Reliab. Eng. Syst. Saf..

[60]  Kesheng Wang,et al.  Wind turbine fault detection based on SCADA data analysis using ANN , 2014 .

[61]  Michael G. Pecht,et al.  IoT-Based Prognostics and Systems Health Management for Industrial Applications , 2016, IEEE Access.

[62]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[63]  Mohammadreza Tahan,et al.  Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review , 2017 .

[64]  George J. Klir,et al.  Uncertainty Modeling and Analysis in Engineering and the Sciences (Hardcover) , 2006 .

[65]  Hedi Dhouibi,et al.  Diagnostic and prognostic of hybrid dynamic systems: Modeling and RUL evaluation for two maintenance policies , 2017, Reliab. Eng. Syst. Saf..

[66]  Belkacem Ould-Bouamama,et al.  A novel gearbox fault feature extraction and classification using Hilbert empirical wavelet transform, singular value decomposition, and SOM neural network , 2018 .

[67]  Hongwen He,et al.  Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter , 2017 .

[68]  Yisha Xiang,et al.  A review on condition-based maintenance optimization models for stochastically deteriorating system , 2017, Reliab. Eng. Syst. Saf..

[69]  J. Lunze,et al.  Diagnosis of Complex Systems: Bridging the Methodologies of the FDI and DX Communities , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[70]  Ke Zhang,et al.  Observer-Based Fault Estimation Techniques , 2017 .

[71]  Qian Liu,et al.  Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process , 2019, Reliab. Eng. Syst. Saf..

[72]  P. Baruah,et al.  HMMs for diagnostics and prognostics in machining processes , 2005 .

[73]  Enrico Zio,et al.  A prognostics approach to nuclear component degradation modeling based on Gaussian Process Regression , 2015 .

[74]  Enrico Sciubba,et al.  Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems , 2004 .

[75]  Weixin Han,et al.  Fault estimation for a quadrotor unmanned aerial vehicle by integrating the parity space approach with recursive least squares , 2018 .

[76]  Luigi Portinale,et al.  Bayesian networks in reliability , 2007, Reliab. Eng. Syst. Saf..

[77]  Weiming Shen,et al.  Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning , 2019, Sensors.

[78]  Kay Chen Tan,et al.  Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics , 2017, IEEE Transactions on Neural Networks and Learning Systems.

[79]  Yan Han,et al.  Multi-level wavelet packet fusion in dynamic ensemble convolutional neural network for fault diagnosis , 2018, Measurement.

[80]  Faisal Khan,et al.  Process Fault Prognosis Using Hidden Markov Model–Bayesian Networks Hybrid Model , 2019, Industrial & Engineering Chemistry Research.

[81]  Mohammad Pourgol-Mohammad,et al.  Design for reliability of automotive systems; case study of dry friction clutch , 2017, Int. J. Syst. Assur. Eng. Manag..

[82]  Ruqiang Yan,et al.  Remaining Useful Life Prediction of Rolling Bearings Using an Enhanced Particle Filter , 2015, IEEE Transactions on Instrumentation and Measurement.

[83]  Bernhard Rinner,et al.  Online monitoring by dynamically refining imprecise models , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[84]  Bin Jiang,et al.  A Review of Fault Detection and Diagnosis for the Traction System in High-Speed Trains , 2020, IEEE Transactions on Intelligent Transportation Systems.

[85]  Enrico Zio,et al.  Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method , 2014, Digit. Signal Process..

[86]  K. Wiesner,et al.  What is a complex system? , 2012, European Journal for Philosophy of Science.

[87]  Dang-Bo Du,et al.  A Prognostic Model Based on DBN and Diffusion Process for Degrading Bearing , 2020, IEEE Transactions on Industrial Electronics.

[88]  Fugee Tsung,et al.  Real-time quality monitoring and diagnosis for manufacturing process profiles based on deep belief networks , 2019, Comput. Ind. Eng..

[89]  Purushottam Gangsar,et al.  Comparative investigation of vibration and current monitoring for prediction of mechanical and electrical faults in induction motor based on multiclass-support vector machine algorithms , 2017 .

[90]  Lei Su,et al.  Fault detection and diagnosis of rotating machinery using modified particle filter , 2017 .

[91]  Weiwen Peng,et al.  Reliability analysis of complex multi-state system with common cause failure based on evidential networks , 2018, Reliab. Eng. Syst. Saf..

[92]  Ming-Chang Lee,et al.  Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress , 2010, ArXiv.

[93]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[94]  Stoyan Stoyanov,et al.  Prognostic and health management for engineering systems: a review of the data-driven approach and algorithms , 2015 .

[95]  Fu-Kwun Wang,et al.  Hybrid approach for remaining useful life prediction of ball bearings , 2019, Qual. Reliab. Eng. Int..

[96]  Cesar H. Comin,et al.  Complex systems: Features, similarity and connectivity , 2016, Physics Reports.

[97]  Shaoping Wang,et al.  An adaptive-order particle filter for remaining useful life prediction of aviation piston pumps , 2017 .

[98]  Mingming Yan,et al.  Bearing remaining useful life prediction using support vector machine and hybrid degradation tracking model. , 2020, ISA transactions.

[99]  David He,et al.  Remaining Useful Life Prediction of Hybrid Ceramic Bearings Using an Integrated Deep Learning and Particle Filter Approach , 2017 .

[100]  Yibing Liu,et al.  A Robust Model-Based Approach for Bearing Remaining Useful Life Prognosis in Wind Turbines , 2020, IEEE Access.

[101]  Ivo Paixao de Medeiros,et al.  Remaining useful life estimation in aeronautics: Combining data-driven and Kalman filtering , 2018, Reliab. Eng. Syst. Saf..

[102]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[103]  Yingwei Zhang,et al.  Fault detection of non-Gaussian processes based on modified independent component analysis , 2010 .

[104]  Furong Gao,et al.  Performance-relevant kernel independent component analysis based operating performance assessment for nonlinear and non-Gaussian industrial processes , 2019 .

[105]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[106]  Hamid Reza Karimi,et al.  A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management , 2016 .

[107]  J. Kwapień,et al.  Physical approach to complex systems , 2012 .

[108]  Wei Qiao,et al.  Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes , 2019, IEEE Transactions on Industrial Electronics.

[109]  Chee Khiang Pang,et al.  Gaussian Mixture Model Using Semisupervised Learning for Probabilistic Fault Diagnosis Under New Data Categories , 2017, IEEE Transactions on Instrumentation and Measurement.

[110]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[111]  Roozbeh Razavi-Far,et al.  Failure Prognosis and Applications—A Survey of Recent Literature , 2019, IEEE Transactions on Reliability.

[112]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[113]  Yaneer Bar-Yam,et al.  Dynamics Of Complex Systems , 2019 .

[114]  Selin Aviyente,et al.  Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings , 2015, IEEE Transactions on Industrial Electronics.

[115]  Kai Goebel,et al.  Bayesian hierarchical model-based prognostics for lithium-ion batteries , 2018, Reliab. Eng. Syst. Saf..

[116]  Cheng Cheng,et al.  Remaining useful life prediction of lithium-ion batteries with adaptive unscented kalman filter and optimized support vector regression , 2020, Neurocomputing.

[117]  Steven X. Ding,et al.  A survey on model-based fault diagnosis for linear discrete time-varying systems , 2018, Neurocomputing.

[118]  Benoît Iung,et al.  Overview on Bayesian networks applications for dependability, risk analysis and maintenance areas , 2012, Eng. Appl. Artif. Intell..

[119]  Zhanpeng Zhang,et al.  A deep belief network based fault diagnosis model for complex chemical processes , 2017, Comput. Chem. Eng..

[120]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[121]  Rolf Isermann,et al.  Fault-Diagnosis Systems , 2005 .

[122]  Fuli Wang,et al.  Online complex nonlinear industrial process operating optimality assessment using modified robust total kernel partial M-regression , 2017 .

[123]  Wei-guo Zhao,et al.  Fault Diagnosis and Prognosis of Bearing Based on Hidden Markov Model with Multi-Features , 2020 .

[124]  Sanchuan Xu.  A survey of knowledge-based intelligent fault diagnosis techniques , 2019 .

[125]  Kaixiang Peng,et al.  A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter , 2019, Neurocomputing.

[126]  Michael R. Brambley,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part II , 2005 .

[127]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[128]  Thomas G. Habetler,et al.  Deep Learning Algorithms for Bearing Fault Diagnosticsx—A Comprehensive Review , 2019, IEEE Access.

[129]  Navid Mostoufi,et al.  Fault diagnosis of chemical processes considering fault frequency via Bayesian network , 2016 .

[130]  Kwon Soon Lee,et al.  Fault Detection and Isolation of Induction Motors Using Recurrent Neural Networks and Dynamic Bayesian Modeling , 2010, IEEE Transactions on Control Systems Technology.

[131]  Linxia Liao,et al.  Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction , 2014, IEEE Transactions on Reliability.

[132]  Jin Guo,et al.  Gearbox Incipient Fault Detection Based on Deep Recursive Dynamic Principal Component Analysis , 2020, IEEE Access.

[133]  Lin Ma,et al.  An integrated approach for system functional reliability assessment using Dynamic Bayesian Network and Hidden Markov Model , 2018, Reliab. Eng. Syst. Saf..

[134]  Changwen Zheng,et al.  Diagnosis Method for Li-Ion Battery Fault Based on an Adaptive Unscented Kalman Filter , 2017 .

[135]  Chao Liu,et al.  Deep Transfer Network with Joint Distribution Adaptation: A New Intelligent Fault Diagnosis Framework for Industry Application , 2018, ISA transactions.

[136]  Neeraj Khera,et al.  Prognostics of aluminum electrolytic capacitors using artificial neural network approach , 2017, Microelectron. Reliab..

[137]  Joseph Mathew,et al.  A review on prognostic techniques for non-stationary and non-linear rotating systems , 2015 .

[138]  Noureddine Zerhouni,et al.  CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks , 2012 .

[139]  Zhu Yongli,et al.  Bayesian networks-based approach for power systems fault diagnosis , 2006, IEEE Transactions on Power Delivery.

[140]  Noureddine Zerhouni,et al.  Remaining Useful Life Estimation of Critical Components With Application to Bearings , 2012, IEEE Transactions on Reliability.

[141]  C. Yoo,et al.  Nonlinear process monitoring using kernel principal component analysis , 2004 .

[142]  B. Bouzouane,et al.  Decision tree and feature selection by using genetic wrapper for fault diagnosis of rotating machinery , 2020 .

[143]  Yaguo Lei,et al.  A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings , 2020, IEEE Transactions on Reliability.

[144]  Wentao Mao,et al.  Predicting Remaining Useful Life of Rolling Bearings Based on Deep Feature Representation and Transfer Learning , 2020, IEEE Transactions on Instrumentation and Measurement.

[145]  Huibin Sun,et al.  A Hybrid Approach to Cutting Tool Remaining Useful Life Prediction Based on the Wiener Process , 2018, IEEE Transactions on Reliability.

[146]  Enrico Zio,et al.  A Kalman Filter-Based Ensemble Approach With Application to Turbine Creep Prognostics , 2012, IEEE Transactions on Reliability.

[147]  C. Joseph Lu,et al.  Using Degradation Measures to Estimate a Time-to-Failure Distribution , 1993 .

[148]  Noureddine Zerhouni,et al.  State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels , 2017 .

[149]  David Kim,et al.  A Bayesian network-based approach for fault analysis , 2017, Expert Syst. Appl..

[150]  Noureddine Zerhouni,et al.  Particle filter-based prognostics: Review, discussion and perspectives , 2016 .

[151]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[152]  Ping Zhang,et al.  Parity relation based fault estimation for nonlinear systems: An LMI approach , 2007, Int. J. Autom. Comput..

[153]  Raymond Reiter,et al.  Characterizing Diagnoses and Systems , 1992, Artif. Intell..

[154]  Takashi Hiyama,et al.  Predicting remaining useful life of rotating machinery based artificial neural network , 2010, Comput. Math. Appl..

[155]  Qunxiong Zhu,et al.  Multivariate time delay analysis based local KPCA fault prognosis approach for nonlinear processes , 2016 .

[156]  Stefan Thurner,et al.  Introduction to the Theory of Complex Systems , 2018, Oxford Scholarship Online.

[157]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[158]  Biqing Huang,et al.  Approach for fault prognosis using recurrent neural network , 2018, J. Intell. Manuf..

[159]  Ratna Babu Chinnam,et al.  A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systems , 2004 .

[160]  Khashayar Khorasani,et al.  A Dual Particle Filter-Based Fault Diagnosis Scheme for Nonlinear Systems , 2018, IEEE Transactions on Control Systems Technology.

[161]  Tim Baines,et al.  State-of-the-art in integrated vehicle health management , 2009 .

[162]  Wenyuan Lv,et al.  A novel method using adaptive hidden semi-Markov model for multi-sensor monitoring equipment health prognosis , 2015 .

[163]  David A. Landgrebe,et al.  A survey of decision tree classifier methodology , 1991, IEEE Trans. Syst. Man Cybern..

[164]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[165]  Steven X. Ding,et al.  Design of robust fuzzy fault detection filter for polynomial fuzzy systems with new finite frequency specifications , 2018, Autom..

[166]  Sarangapani Jagannathan,et al.  A Model-Based Fault Detection and Prognostics Scheme for Takagi–Sugeno Fuzzy Systems , 2014, IEEE Transactions on Fuzzy Systems.

[167]  N. Balakrishnan,et al.  Bivariate degradation analysis of products based on Wiener processes and copulas , 2013 .

[168]  Yong Sun,et al.  A review on degradation models in reliability analysis , 2010, WCE 2010.

[169]  Huijun Gao,et al.  Data-Based Techniques Focused on Modern Industry: An Overview , 2015, IEEE Transactions on Industrial Electronics.

[170]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[171]  Enrico Zio,et al.  A particle filtering and kernel smoothing-based approach for new design component prognostics , 2015, Reliab. Eng. Syst. Saf..

[172]  Dawn An,et al.  Practical options for selecting data-driven or physics-based prognostics algorithms with reviews , 2015, Reliab. Eng. Syst. Saf..

[173]  Tomasz Walkowiak,et al.  Complex Systems and Dependability , 2012 .

[174]  Guangzhong Dong,et al.  Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression , 2018, IEEE Transactions on Industrial Electronics.

[175]  Lei Huang,et al.  Bayesian Networks in Fault Diagnosis , 2017, IEEE Transactions on Industrial Informatics.

[176]  Gunnar Rätsch,et al.  An introduction to kernel-based learning algorithms , 2001, IEEE Trans. Neural Networks.

[177]  Xingsheng Gu,et al.  Multi-block statistics local kernel principal component analysis algorithm and its application in nonlinear process fault detection , 2020, Neurocomputing.

[178]  Yaguo Lei,et al.  Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery , 2020, Neurocomputing.

[179]  Minsu Kim,et al.  Early Fault Diagnosis and Classification of Ball Bearing Using Enhanced Kurtogram and Gaussian Mixture Model , 2019, IEEE Transactions on Instrumentation and Measurement.

[180]  Hongwen He,et al.  Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries , 2018, IEEE Transactions on Vehicular Technology.

[181]  Oliver Schütze,et al.  A Neural Network-Evolutionary Computational Framework for Remaining Useful Life Estimation of Mechanical Systems , 2019, Neural Networks.

[182]  Yaguo Lei,et al.  Deep normalized convolutional neural network for imbalanced fault classification of machinery and its understanding via visualization , 2018, Mechanical Systems and Signal Processing.

[183]  Xuefeng Chen,et al.  Dislocated Time Series Convolutional Neural Architecture: An Intelligent Fault Diagnosis Approach for Electric Machine , 2017, IEEE Transactions on Industrial Informatics.

[184]  Weiwen Peng,et al.  Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network , 2019, IEEE Transactions on Industrial Electronics.

[185]  Lipo Wang.  Support vector machines : theory and applications , 2005 .

[186]  N.D.R. Sarma,et al.  A fuzzy BP approach for diagnosis and prognosis of bearing faults in induction motors , 2005, IEEE Power Engineering Society General Meeting, 2005.

[187]  Imed Jlassi,et al.  A Robust Observer-Based Method for IGBTs and Current Sensors Fault Diagnosis in Voltage-Source Inverters of PMSM Drives , 2017, IEEE Transactions on Industry Applications.

[188]  Khaoula Ben Abdellafou,et al.  Fault detection of uncertain nonlinear process using reduced interval kernel principal component analysis (RIKPCA) , 2020 .

[189]  Joseph Mathew,et al.  Rotating machinery prognostics. State of the art, challenges and opportunities , 2009 .

[190]  Jong-Myon Kim,et al.  Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm , 2019, Energies.

[191]  Yuxin Cui,et al.  Transfer Learning with Deep Recurrent Neural Networks for Remaining Useful Life Estimation , 2018, Applied Sciences.

[192]  Jing Tian,et al.  Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis , 2016, IEEE Transactions on Industrial Electronics.

[193]  Kai Goebel,et al.  A neural network filtering approach for similarity-based remaining useful life estimation , 2018, The International Journal of Advanced Manufacturing Technology.

[194]  Yuehua Cheng,et al.  Dynamic Long Short-Term Memory Neural-Network- Based Indirect Remaining-Useful-Life Prognosis for Satellite Lithium-Ion Battery , 2018, Applied Sciences.

[195]  Rui Kang,et al.  Benefits and Challenges of System Prognostics , 2012, IEEE Transactions on Reliability.

[196]  S. Lloyd,et al.  Measures of complexity: a nonexhaustive list , 2001 .

[197]  June Ho Park,et al.  A novel hybrid of auto-associative kernel regression and dynamic independent component analysis for fault detection in nonlinear multimode processes , 2018, Journal of Process Control.

[198]  Mohammad Pourgolmohamad,et al.  Probabilistic Physics of Failure (PPOF) Reliability Analysis of RF-MEMS Switches under Uncertainty , 2018 .