Comparison of Computational Prognostic Methods for Complex Systems Under Dynamic Regimes: A Review of Perspectives

Complex systems are expected to play a key role in the progress of Prognostics Health Management but the breadth of technologies that will highlight gaps in the dynamic regimes are expected to become more prominent and likely more challenging in the future. The design and implementation of sophisticated computational algorithms have become a critical aspect to solve problems in many prognostic applications for multiple regimes. In addition to a wide variety of conventional computational and cognitive paradigms such as machine learning and data mining fields, specific applications in prognostics have led to a wealth of newly proposed methods and techniques. This paper reviews practices for modeling prognostics and remaining useful life applications in complex systems working under multiple operational regimes. An analysis is provided to compare and combine the findings of previously published studies in the literature, and it assesses the effectiveness of techniques for different stages of prognostic development. The paper concludes with some speculations on the likely advances in fusion of advanced methods for case specific modeling.

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

[2]  George J. Vachtsevanos,et al.  A particle-filtering approach for on-line fault diagnosis and failure prognosis , 2009 .

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

[4]  David A. Freedman,et al.  Statistical Models: Theory and Practice: References , 2005 .

[5]  Jeffrey A. Jones,et al.  Reconstructing secondary test database from PHM08 challenge data set , 2018, Data in brief.

[6]  Carey Bunks,et al.  CONDITION-BASED MAINTENANCE OF MACHINES USING HIDDEN MARKOV MODELS , 2000 .

[7]  Emmanuel Ramasso,et al.  Investigating computational geometry for failure prognostics , 2014, International Journal of Prognostics and Health Management.

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

[9]  Frank L. Lewis,et al.  Intelligent Fault Diagnosis and Prognosis for Engineering Systems , 2006 .

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

[11]  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.

[12]  S. Haykin,et al.  Making sense of a complex world [chaotic events modeling] , 1998, IEEE Signal Process. Mag..

[13]  Michael Pecht,et al.  Study of ensemble learning-based fusion prognostics , 2010, 2010 Prognostics and System Health Management Conference.

[14]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[15]  A. Günel,et al.  D8.1 - Statistical and Semantic Multisensor Data Evaluation for Fluid Condition Monitoring in Wind Turbines , 2013 .

[16]  Steven Y. Liang,et al.  STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS , 2000 .

[17]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[18]  M. Koppen.  No-Free-Lunch theorems and the diversity of algorithms , 2004 .

[19]  L. Peel,et al.  Data driven prognostics using a Kalman filter ensemble of neural network models , 2008, 2008 International Conference on Prognostics and Health Management.

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

[21]  Kenneth A. Loparo,et al.  Physically based diagnosis and prognosis of cracked rotor shafts , 2002, SPIE Defense + Commercial Sensing.

[22]  Steven Y. Liang,et al.  Adaptive Prognostics for Rolling Element Bearing Condition , 1999 .

[23]  Simon Haykin,et al.  Detection of signals in chaos , 1995, Proc. IEEE.

[24]  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.

[25]  Brigitte Chebel-Morello,et al.  Remaining useful life estimation based on discriminating shapelet extraction , 2015, Reliab. Eng. Syst. Saf..

[26]  Emmanuel Ramasso,et al.  Investigating Computational Geometry for Failure Prognostics in Presence of Imprecise Health Indicator: Results and Comparisons on C-MAPSS Datasets , 2014 .

[27]  K. Dmitry,et al.  An algorithm for rule generation in fuzzy expert systems , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[28]  P. C. Paris,et al.  A Critical Analysis of Crack Propagation Laws , 1963 .

[29]  Abhinav Saxena,et al.  Damage propagation modeling for aircraft engine run-to-failure simulation , 2008, 2008 International Conference on Prognostics and Health Management.

[30]  Czesław Cempel.  Generalized singular value decomposition in multidimensional condition monitoring of machines—A proposal of comparative diagnostics , 2009 .

[31]  Jeffrey Alun Jones,et al.  NARX time series model for remaining useful life estimation of gas turbine engines , 2016 .

[32]  K. Goebel,et al.  Standardizing research methods for prognostics , 2008, 2008 International Conference on Prognostics and Health Management.

[33]  Dawn An,et al.  Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab , 2013, Reliab. Eng. Syst. Saf..

[34]  George Chryssolouris,et al.  On a Predictive Maintenance Platform for Production Systems , 2012 .

[35]  Guilherme De A. Barreto,et al.  Long-term time series prediction with the NARX network: An empirical evaluation , 2008, Neurocomputing.

[36]  Jianbo Yu,et al.  A similarity-based prognostics approach for Remaining Useful Life estimation of engineered systems , 2008, 2008 International Conference on Prognostics and Health Management.

[37]  Michael J. Roemer,et al.  A Comprehensive Prognostics Approach for Predicting Gas Turbine Engine Bearing Life , 2004 .

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

[39]  Rui Kang,et al.  Benefits analysis of prognostics in systems , 2010, 2010 Prognostics and System Health Management Conference.

[40]  Bin Zaidan,et al.  Bayesian Approaches for Complex System Prognostics , 2014 .

[41]  Janez Bester,et al.  Introduction to the Artificial Neural Networks , 2011 .

[42]  Kai Goebel,et al.  When will it break? A hybrid soft computing model to predict time-to-break margins in paper machines , 2002, Optics + Photonics.

[43]  C. Cempel.  MULTIDIMENSIONAL CONDITION MONITORING OF MECHANICAL SYSTEMS IN OPERATION , 2003 .

[44]  Enrico Zio,et al.  Ensemble neural network-based particle filtering for prognostics , 2013 .

[45]  C.S. Byington,et al.  Data-driven neural network methodology to remaining life predictions for aircraft actuator components , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[46]  G. Krishnaiah,et al.  Neural network approach for a combined performance and mechanical health monitoring of a gas turbine engine , 2012 .

[47]  Shun-ichi Amari,et al.  Network information criterion-determining the number of hidden units for an artificial neural network model , 1994, IEEE Trans. Neural Networks.

[48]  S. Marble,et al.  Predicting the remaining life of propulsion system bearings , 2006, 2006 IEEE Aerospace Conference.

[49]  Noureddine Zerhouni,et al.  Features Selection Procedure for Prognostics: An Approach Based on Predictability , 2012 .

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

[51]  Mohammad Jamshidi,et al.  System of systems engineering : innovations for the 21st century , 2008 .

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

[53]  T. Bayes.  An essay towards solving a problem in the doctrine of chances , 2003 .

[54]  Manzar Abbas.  System-level health assessment of complex engineered processes , 2010 .

[55]  Jeffrey Alun Jones,et al.  The use of a discrete event simulation to model the achievement of maintenance free operating time for aerospace systems , 2001, Annual Reliability and Maintainability Symposium. 2001 Proceedings. International Symposium on Product Quality and Integrity (Cat. No.01CH37179).

[56]  K. L. Butler.  An expert system based framework for an incipient failure detection and predictive maintenance system , 1996, Proceedings of International Conference on Intelligent System Application to Power Systems.

[57]  Jeremy Sheldon,et al.  Prognostics/diagnostics for gas turbine engine bearings , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

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

[59]  Enrico Zio,et al.  Echo State Network for the Remaining Useful Life Prediction of a Turbofan Engine , 2016, PHM Society European Conference.

[60]  F.O. Heimes,et al.  Recurrent neural networks for remaining useful life estimation , 2008, 2008 International Conference on Prognostics and Health Management.

[61]  Jae Sik Chung,et al.  A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation , 2010 .

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

[63]  Jeffrey Alun Jones,et al.  Reducing Dimensionality of Multi-regime Data for Failure Prognostics , 2017, Journal of Failure Analysis and Prevention.

[64]  Noureddine Zerhouni,et al.  Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction , 2016, J. Intell. Manuf..

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

[66]  P. Djurić,et al.  Particle filtering , 2003, IEEE Signal Process. Mag..

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

[68]  Noureddine Zerhouni,et al.  Data-driven prognostics based on health indicator construction: Application to PRONOSTIA's data , 2013, 2013 European Control Conference (ECC).

[69]  Tianyi Wang,et al.  Trajectory Similarity Based Prediction for Remaining Useful Life Estimation , 2010 .

[70]  S. Chatterjee,et al.  Influential Observations, High Leverage Points, and Outliers in Linear Regression , 1986 .

[71]  Mark Beale,et al.  Neural Network Toolbox™ User's Guide , 2015 .

[72]  M.G. Pecht,et al.  Prognostics and health management of electronics , 2008, IEEE Transactions on Components and Packaging Technologies.

[73]  Ian K. Jennions,et al.  A Similarity-Based Prognostics Approach for Remaining Useful Life Prediction , 2014 .

[74]  Mohammad Jamshidi,et al.  Systems of Systems Engineering: Principles and Applications , 2008 .

[75]  Wei Liang,et al.  Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..

[76]  Ömer Faruk Eker.  A Hybrid Prognostic Methodology and its Application to Well-Controlled Engineering Systems , 2015 .

[77]  Abhinav Saxena,et al.  Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets , 2020, International Journal of Prognostics and Health Management.

[78]  Czesław Cempel,et al.  Simple condition forecasting techniques in vibroacoustical diagnostics , 1987 .

[79]  Oguz Bektas.  An adaptive data filtering model for remaining useful life estimation , 2018 .

[80]  Jose C. Principe,et al.  Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM , 1999 .

[81]  Yu Peng,et al.  A modified echo state network based remaining useful life estimation approach , 2012, 2012 IEEE Conference on Prognostics and Health Management.

[82]  Yu-Chi Ho,et al.  Simple Explanation of the No Free Lunch Theorem of Optimization , 2001 .

[83]  Ming Rao,et al.  Fuzzy expert system for real-time process condition monitoring and incident prevention , 1998 .

[84]  Zoubin Ghahramani,et al.  An Introduction to Hidden Markov Models and Bayesian Networks , 2001, Int. J. Pattern Recognit. Artif. Intell..

[85]  A. Maćkiewicz,et al.  Principal Components Analysis (PCA) , 1993 .

[86]  D. C. Swanson,et al.  A general prognostic tracking algorithm for predictive maintenance , 2001, 2001 IEEE Aerospace Conference Proceedings (Cat. No.01TH8542).

[87]  Peter Whittle,et al.  Hypothesis Testing in Time Series Analysis. , 1951 .

[88]  B. J. Roylance,et al.  Plant machinery working life prediction method utilizing reliability and condition-monitoring data , 2000 .

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

[90]  Michael J. Roemer,et al.  Predicting remaining life by fusing the physics of failure modeling with diagnostics , 2004 .

[91]  David G. Ward,et al.  Helicopter gearbox diagnostics and prognostics using vibration signature analysis , 1993, Defense, Security, and Sensing.

[92]  Panos Vassiliadis,et al.  Modeling multidimensional databases, cubes and cube operations , 1998, Proceedings. Tenth International Conference on Scientific and Statistical Database Management (Cat. No.98TB100243).

[93]  Emmanuel Ramasso,et al.  Contribution of belief functions to hidden markov models with an application to fault diagnosis , 2009, 2009 IEEE International Workshop on Machine Learning for Signal Processing.