Diagnostics and prognostics for complex systems: A review of methods and challenges
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Felician Campean | Morteza Soleimani | Daniel Neagu | F. Campean | Daniela Neagu | Morteza Soleimani
[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 .