Review on Health Indices Extraction and Trend Modeling for Remaining Useful Life Estimation

Scientific research in the area of fault prognosis is increasingly focused on estimating the Remaining Useful Life of equipment, since its knowledge is a key input to the scheduling of Condition-Based and Predictive Maintenance. Several research studies have been directed to developing methods for modeling the trend of health indicators for Remaining Useful Life estimation, this paper makes a review of these approaches. Fault diagnosis methods sensitive to the progressive evolution of degradation phenomena are presented and their usability for fault prognosis is discussed. Then, methods for modeling the trends of health indicators are analyzed to highlight the selection criteria of the modeling methods, according to the available information on the operating conditions of the systems, and on the degradation phenomenon. Finally, some reflections are made regarding the elements that prevent the large-scale use of prognostics in industry today, and on the integration of prognostics in risk assessment and management.

[1]  Krishna R. Pattipati,et al.  Reasoning and modeling systems in diagnosis and prognosis , 2001, SPIE Defense + Commercial Sensing.

[2]  Lina Bertling Tjernberg,et al.  An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings , 2015, IEEE Transactions on Smart Grid.

[3]  Dejie Yu,et al.  Application of EMD method and Hilbert spectrum to the fault diagnosis of roller bearings , 2005 .

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

[5]  J. Ragot,et al.  Parameter estimation for uncertain systems based on fault diagnosis using Takagi-Sugeno model. , 2015, ISA transactions.

[6]  M. A. Djeziri,et al.  Hybrid method for remaining useful life prediction in wind turbine systems , 2018 .

[7]  Dennis S. Bernstein,et al.  Finite-Time Stability of Continuous Autonomous Systems , 2000, SIAM J. Control. Optim..

[8]  Enrico Zio,et al.  Ensemble-approaches for clustering health status of oil sand pumps , 2012, Expert Syst. Appl..

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

[10]  Danwei Wang,et al.  Model-based Health Monitoring of Hybrid Systems , 2013, Springer New York.

[11]  George Vachtsevanos,et al.  Fault prognosis using dynamic wavelet neural networks , 2001, 2001 IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. (Cat. No.01CH37237).

[12]  C. James Li,et al.  Gear fatigue crack prognosis using embedded model, gear dynamic model and fracture mechanics , 2005 .

[13]  Enrico Zio,et al.  Condition-based probabilistic safety assessment of a spontaneous steam generator tube rupture accident scenario , 2018 .

[14]  Michael Pecht,et al.  Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model , 2014 .

[15]  T. Yoneyama,et al.  Prognostics performance metrics and their relation to requirements, design, verification and cost-benefit , 2008, 2008 International Conference on Prognostics and Health Management.

[16]  Wilfrid Perruquetti,et al.  A Global High-Gain Finite-Time Observer , 2010, IEEE Transactions on Automatic Control.

[17]  Jen Tang,et al.  Determination of burn‐in parameters and residual life for highly reliable products , 2003 .

[18]  Enrico Zio,et al.  A data-driven approach for predicting failure scenarios in nuclear systems , 2010 .

[19]  Michael J. Roemer,et al.  Advanced diagnostics and prognostics for gas turbine engine risk assessment , 2000, 2000 IEEE Aerospace Conference. Proceedings (Cat. No.00TH8484).

[20]  David,et al.  Switching Kalman filter for failure prognostic , 2015 .

[21]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  David He,et al.  A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology , 2007 .

[23]  Matthew Daigle,et al.  A Model-Based Prognostics Approach Applied to Pneumatic Valves , 2011 .

[24]  Wei Wu,et al.  Prognostics of Machine Health Condition using an Improved ARIMA-based Prediction method , 2007, 2007 2nd IEEE Conference on Industrial Electronics and Applications.

[25]  Douglas E. Adams,et al.  A nonlinear dynamical systems framework for structural diagnosis and prognosis , 2002 .

[26]  Steven X. Ding,et al.  A Survey of Fault Diagnosis and Fault-Tolerant Techniques—Part I: Fault Diagnosis With Model-Based and Signal-Based Approaches , 2015, IEEE Transactions on Industrial Electronics.

[27]  Enrico Zio,et al.  Remaining useful life estimation in heterogeneous fleets working under variable operating conditions , 2016, Reliab. Eng. Syst. Saf..

[28]  F. Allgöwer,et al.  Finite time convergent observers for nonlinear systems , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[29]  Sheng-Tsaing Tseng,et al.  Stochastic Diffusion Modeling of Degradation Data , 2007, Journal of Data Science.

[30]  Hongwen He,et al.  Lithium-Ion Battery Remaining Useful Life Prediction With Box–Cox Transformation and Monte Carlo Simulation , 2019, IEEE Transactions on Industrial Electronics.

[31]  J. Gertler Fault detection and isolation using parity relations , 1997 .

[32]  M. Fliess Some basic structural properties of generalized linear systems , 1991 .

[33]  Julio,et al.  Software Package Evaluation for Lyapunov Exponent and Others Features of Signals Evaluating Condition Monitoring Performance of Nonlinear Dynamic Systems , 2015 .

[34]  Uday Kumar,et al.  Remaining Useful Life Estimation using Time Trajectory Tracking and Support Vector Machines , 2012 .

[35]  M. Abdel-Hameed A Gamma Wear Process , 1975, IEEE Transactions on Reliability.

[36]  Shankar Sankararaman,et al.  Significance, interpretation, and quantification of uncertainty in prognostics and remaining useful life prediction , 2015 .

[37]  Oussama Djedidi,et al.  Failure Prognosis of Embedded Systems Based on Temperature Drift Assessment , 2019 .

[38]  V. Makis,et al.  Condition monitoring and classification of rotating machinery using wavelets and hidden Markov models , 2007 .

[39]  E. Moulay,et al.  Finite time stability of nonlinear systems , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[40]  Enrico Zio,et al.  Model-based Monte Carlo state estimation for condition-based component replacement , 2009, Reliab. Eng. Syst. Saf..

[41]  E. Çinlar,et al.  STOCHASTIC PROCESS FOR EXTRAPOLATING CONCRETE CREEP , 1977 .

[42]  Jay Lee,et al.  Intelligent prognostics tools and e-maintenance , 2006, Comput. Ind..

[43]  Moamar Sayed Mouchaweh,et al.  Fault Prognostics for the Predictive Maintenance of Wind Turbines: State of the Art , 2018, DMLE/IOTSTREAMING@PKDD/ECML.

[44]  Belkacem Ould-Bouamama,et al.  Particle filter based hybrid prognostics for health monitoring of uncertain systems in bond graph framework , 2016 .

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

[46]  Zhiguo Zeng,et al.  A classification-based framework for trustworthiness assessment of quantitative risk analysis , 2017 .

[47]  Jan M. van Noortwijk,et al.  A survey of the application of gamma processes in maintenance , 2009, Reliab. Eng. Syst. Saf..

[48]  Belkacem Ould Bouamama,et al.  Sensor fault detection of energetic system using modified parity space approach , 2007, 2007 46th IEEE Conference on Decision and Control.

[49]  Enrico Zio,et al.  Challenges in the vulnerability and risk analysis of critical infrastructures , 2016, Reliab. Eng. Syst. Saf..

[50]  Dan M. Frangopol,et al.  Probabilistic models for life‐cycle performance of deteriorating structures: review and future directions , 2004 .

[51]  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.

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

[53]  Belkacem Ould Bouamama,et al.  Bond Graph Approach for Plant Fault Detection and Isolation: Application to Intelligent Autonomous Vehicle , 2014, IEEE Transactions on Automation Science and Engineering.

[54]  M.J. Roemer,et al.  Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft] , 2002, Proceedings, IEEE Aerospace Conference.

[55]  M. Crowder,et al.  Covariates and Random Effects in a Gamma Process Model with Application to Degradation and Failure , 2004, Lifetime data analysis.

[56]  H. D. Miller,et al.  The Theory Of Stochastic Processes , 1977, The Mathematical Gazette.

[57]  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.

[58]  Noureddine Zerhouni,et al.  The ISO 13381-1 standard's failure prognostics process through an example , 2010, 2010 Prognostics and System Health Management Conference.

[59]  Dennis S. Bernstein,et al.  Geometric homogeneity with applications to finite-time stability , 2005, Math. Control. Signals Syst..

[60]  Nacer K. M'Sirdi,et al.  Data-Driven Approach for Feature Drift Detection in Embedded Electronic Devices , 2018 .

[61]  A. H. Christer,et al.  A state space condition monitoring model for furnace erosion prediction and replacement , 1997 .

[62]  F. Allgower,et al.  A Finite Time Unknown Input Observer For Linear Systems , 2006, 2006 14th Mediterranean Conference on Control and Automation.

[63]  Xiao Wang,et al.  Wiener processes with random effects for degradation data , 2010, J. Multivar. Anal..

[64]  Michael Thurston,et al.  Standards Developments for Condition-Based Maintenance Systems , 2001 .

[65]  E. Zio,et al.  Prognostics and Health Management of Industrial Equipment , 2013 .

[66]  X. Xia,et al.  Semi-global finite-time observers for nonlinear systems , 2008, Autom..

[67]  Sofiane Achiche,et al.  Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1: System description , 2013, Appl. Soft Comput..

[68]  George Vachtsevanos,et al.  A Particle Filtering Framework for Failure Prognosis , 2005 .

[69]  Nacer K. M'Sirdi,et al.  Fault prognosis based on physical and stochastic models , 2016, 2016 European Control Conference (ECC).

[70]  Dragan Banjevic,et al.  Remaining useful life in theory and practice , 2009 .

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

[72]  K. Goebel,et al.  Metrics for evaluating performance of prognostic techniques , 2008, 2008 International Conference on Prognostics and Health Management.

[73]  Lei Guo,et al.  Robust bearing performance degradation assessment method based on improved wavelet packet–support vector data description , 2009 .

[74]  Dragan Banjevic,et al.  Calculation of reliability function and remaining useful life for a Markov failure time process , 2006 .

[75]  Miguel A. Sanz-Bobi,et al.  SIMAP: Intelligent System for Predictive Maintenance: Application to the health condition monitoring of a windturbine gearbox , 2006, Comput. Ind..

[76]  M. A. Djeziri,et al.  Wavelet decomposition applied to fluid leak detection and isolation in presence of disturbances , 2012, 2012 20th Mediterranean Conference on Control & Automation (MED).

[77]  Taejung Yeo,et al.  A novel multistage Support Vector Machine based approach for Li ion battery remaining useful life estimation , 2015 .

[78]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[79]  M. R. James Finite time observers and observability , 1990, 29th IEEE Conference on Decision and Control.

[80]  Benoît Iung,et al.  Remaining useful life estimation based on stochastic deterioration models: A comparative study , 2013, Reliab. Eng. Syst. Saf..

[81]  Gang Li,et al.  Reconstruction based fault prognosis for continuous processes , 2010 .

[82]  Cédric Join,et al.  Robust residual generation for linear fault diagnosis: an algebraic setting with examples , 2004 .

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

[84]  Jean-Pierre Barbot,et al.  An algebraic framework for the design of nonlinear observers with unknown inputs , 2007, 2007 46th IEEE Conference on Decision and Control.

[85]  Ming J. Zuo,et al.  GEARBOX FAULT DIAGNOSIS USING ADAPTIVE WAVELET FILTER , 2003 .

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

[87]  Ruoyu Li,et al.  Plastic Bearing Fault Diagnosis Based on a Two-Step Data Mining Approach , 2013, IEEE Transactions on Industrial Electronics.

[88]  Alan S. Perelson,et al.  System Dynamics: A Unified Approach , 1976, IEEE Transactions on Systems, Man, and Cybernetics.

[89]  Joseph Mathew,et al.  Multiple Band-Pass Autoregressive Demodulation for Rolling-Element Bearing Fault Diagnosis , 2001 .

[90]  E. Moulay,et al.  Finite time stability and stabilization of a class of continuous systems , 2006 .

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

[92]  Rafael Martínez-Guerra,et al.  On nonlinear systems diagnosis using differential and algebraic methods , 2008, J. Frankl. Inst..

[93]  L. Fridman,et al.  Higher‐order sliding‐mode observer for state estimation and input reconstruction in nonlinear systems , 2008 .

[94]  F. Liu SYNTHÈSES D'OBSERVATEURS A ENTREES INCONNUES POUR LES SYSTÈMES NON LINEAIRES , 2007 .

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

[96]  Jamie B. Coble,et al.  Merging Data Sources to Predict Remaining Useful Life – An Automated Method to Identify Prognostic Parameters , 2010 .

[97]  Joseph R. Cavallaro,et al.  Derivation and application of nonlinear analytical redundancy techniques with applications to robotics , 2002 .

[98]  Mustapha Ouladsine,et al.  Degradation modelling with operating mode changes , 2015, 2015 IEEE Conference on Prognostics and Health Management (PHM).

[99]  A.P. Wang,et al.  Fault diagnosis for nonlinear systems via neural networks and parameter estimation , 2005, 2005 International Conference on Control and Automation.

[100]  Qiang Miao,et al.  Health monitoring of cooling fan bearings based on wavelet filter , 2015 .

[101]  AchicheSofiane,et al.  Wind turbine condition monitoring based on SCADA data using normal behavior models. Part 1 , 2013 .

[102]  D. M. Frangopol,et al.  Bridge Maintenance, Safety, Management and Cost : Proceedings of the 2nd International Conference of the International Association for Bridge Maintenance and Safety, Kyoto, Japan, 18-22 October, 2004 - IABMAS '04 , 2014 .

[103]  Castelli Marcelo,et al.  Fault Diagnosis of Induction Motors Based on FFT , 2012 .

[104]  Wenbin Wang,et al.  A model for residual life prediction based on Brownian motion with an adaptive drift , 2011, Microelectron. Reliab..

[105]  Benoît Iung,et al.  Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system , 2008, Reliab. Eng. Syst. Saf..

[106]  Mohand Djeziri,et al.  Remaining useful life estimation without needing for prior knowledge of the degradation features , 2017 .

[107]  Robert Engel,et al.  A continuous-time observer which converges in finite time , 2002, IEEE Trans. Autom. Control..

[108]  D. Lefebvre,et al.  Parameter Estimation for Fault Diagnosis in Nonlinear Systems by ANFIS , 2012 .

[109]  Mustapha Ouladsine,et al.  Health Index Extraction Methods for Batch Processes in Semiconductor Manufacturing , 2015, IEEE Transactions on Semiconductor Manufacturing.

[110]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[111]  W. J. Padgett,et al.  Accelerated Degradation Models for Failure Based on Geometric Brownian Motion and Gamma Processes , 2005, Lifetime data analysis.

[112]  Piero Baraldi,et al.  Differential evolution-based multi-objective optimization for the definition of a health indicator for fault diagnostics and prognostics , 2018 .

[113]  Sylvain Verron,et al.  Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges , 2016, Annu. Rev. Control..

[114]  Christophe Berenguer,et al.  Condition based maintenance model for a production deteriorating system , 2010, 2010 Conference on Control and Fault-Tolerant Systems (SysTol).

[115]  Sankalita Saha,et al.  On Applying the Prognostic Performance Metrics , 2009 .

[116]  Jay Lee,et al.  A prognostic algorithm for machine performance assessment and its application , 2004 .

[117]  Mustapha Ouladsine,et al.  Fault Detection and Isolation in Marine Diesel Engines: A Generic Methodology , 2012 .