Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction

Prognostics focuses on predicting the future performance of a system, specifically the time at which the system no long performs its desired functionality, its time to failure. As an important aspect of prognostics, remaining useful life (RUL) prediction estimates the remaining usable life of a system, which is essential for maintenance decision making and contingency mitigation. A significant amount of research has been reported in the literature to develop prognostics models that are able to predict a system's RUL. These models can be broadly categorized into experience-based models, date-driven models, and physics-based models. However, due to system complexity, data availability, and application constraints, there is no universally accepted best model to estimate RUL. The review part of this paper specifically focused on the development of hybrid prognostics approaches, attempting to leverage the advantages of combining the prognostics models in the aforementioned different categories for RUL prediction. The hybrid approaches reported in the literature were systematically classified by the combination and interfaces of various types of prognostics models. In the case study part, a hybrid prognostics method was proposed and applied to a battery degradation case to show the potential benefit of the hybrid prognostics approach.

[1]  Lei Xu,et al.  Health management based on fusion prognostics for avionics systems , 2011 .

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

[3]  Michael Pecht,et al.  A comparative review of prognostics-based reliability methods for Lithium batteries , 2011, 2011 Prognostics and System Health Managment Confernece.

[4]  J. Celaya,et al.  Prognostics of Power Mosfets Under Thermal Stress Accelerated Aging Using Data-Driven and Model-Based Methodologies , 2011 .

[5]  Yu Peng,et al.  Lithium-ion Battery Remaining Useful Life Estimation Based on Nonlinear AR Model Combined with Degradation Feature , 2012 .

[6]  Jay Lee,et al.  Feature signature prediction of a boring process using neural network modeling with confidence bounds , 2006 .

[7]  Giulio Gola,et al.  From measurement collection to remaining useful life estimation : defining a diagnostic-prognostic frame for optimal maintenance scheduling of choke valves undergoing erosion , 2011 .

[8]  Chaochao Chen,et al.  Machine remaining useful life prediction: An integrated adaptive neuro-fuzzy and high-order particle filtering approach , 2012 .

[9]  Michael G. Pecht,et al.  A fusion prognostics method for remaining useful life prediction of electronic products , 2009, 2009 IEEE International Conference on Automation Science and Engineering.

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

[11]  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.

[12]  Thomas G. Dietterich.  Multiple Classifier Systems , 2000, Lecture Notes in Computer Science.

[13]  Marcos Eduardo Orchard,et al.  A Particle Filtering-based Framework for On-line Fault Diagnosis and Failure Prognosis , 2007 .

[14]  Shien-Ming Wu,et al.  Time series and system analysis with applications , 1983 .

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

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

[17]  K. Goebel,et al.  An integrated approach to battery health monitoring using bayesian regression and state estimation , 2007, 2007 IEEE Autotestcon.

[18]  Jingjing He,et al.  Integrated fatigue damage diagnosis and prognosis under uncertainties , 2012, Annual Conference of the PHM Society.

[19]  Qing Li,et al.  A Hybrid LSSVR-HMM Based Prognostics Approach , 2012 .

[20]  Michael G. Pecht,et al.  A prognostics and health management roadmap for information and electronics-rich systems , 2010, Microelectron. Reliab..

[21]  K. Goebel,et al.  Fusing competing prediction algorithms for prognostics , 2006, 2006 IEEE Aerospace Conference.

[22]  Jay Lee,et al.  Similarity based method for manufacturing process performance prediction and diagnosis , 2007, Comput. Ind..

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

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

[25]  Lifeng Xi,et al.  Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .

[26]  Christina Willhauck,et al.  Mixed Gaussian process and state-space approach for fatigue crack growth prediction , 2007 .

[27]  Eric Moulines,et al.  Comparison of resampling schemes for particle filtering , 2005, ISPA 2005. Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, 2005..

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

[29]  Michael G. Pecht,et al.  A hybrid prognostics methodology for electronic products , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[30]  George J. Vachtsevanos,et al.  A Particle Filtering Approach for On-Line Failure Prognosis in a Planetary Carrier Plate , 2007, Int. J. Fuzzy Log. Intell. Syst..

[31]  Jay Lee,et al.  A Hybrid Method for On-line Performance Assessment and Life Prediction in Drilling Operations , 2007, 2007 IEEE International Conference on Automation and Logistics.

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

[33]  Zhigang Tian,et al.  Uncertainty Quantification in Gear Remaining Useful Life Prediction Through an Integrated Prognostics Method , 2013, IEEE Transactions on Reliability.

[34]  Ying Peng,et al.  A hybrid approach of HMM and grey model for age-dependent health prediction of engineering assets , 2011, Expert Syst. Appl..

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

[36]  Taejin Kim,et al.  Online Estimation of Lithium-Ion Battery State-of-Charge and Capacity with a Multiscale Filtering Technique , 2011 .

[37]  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.

[38]  Bin Zhang,et al.  Machine Condition Prediction Based on Adaptive Neuro–Fuzzy and High-Order Particle Filtering , 2011, IEEE Transactions on Industrial Electronics.

[39]  Nazih Mechbal,et al.  An Approach to the Health Monitoring of a Pumping Unit in an Aircraft Engine Fuel System , 2012 .

[40]  K. Goebel,et al.  Prognostics in Battery Health Management , 2008, IEEE Instrumentation & Measurement Magazine.

[41]  H. A. Sutherland,et al.  Prognostics, a new look at statistical life prediction for condition-based maintenance , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[42]  Peng Wang,et al.  Fault prognostics using dynamic wavelet neural networks , 2001, Artificial Intelligence for Engineering Design, Analysis and Manufacturing.

[43]  David C. Swanson,et al.  PROGNOSTIC MODELLING OF CRACK GROWTH IN A TENSIONED STEEL BAND , 2000 .

[44]  George Vachtsevanos,et al.  A Novel Architecture for an Integrated Fault Diagnostic/Prognostic System , 2002 .

[45]  Sankaran Mahadevan,et al.  Prognostics and Health Monitoring in the Presence of Heterogeneous Information , 2012 .

[46]  B. Saha,et al.  Uncertainty Management for Diagnostics and Prognostics of Batteries using Bayesian Techniques , 2008, 2008 IEEE Aerospace Conference.

[47]  K. Goebel,et al.  Improving Computational Efficiency of Prediction in Model-Based Prognostics Using the Unscented Transform , 2010 .

[48]  Nagi Gebraeel,et al.  Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.

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

[50]  Enrico Zio,et al.  Fatigue crack growth prognostics by Particle Filtering application and Ensemble of Neural Network , 2012 .

[51]  Huiguo Zhang,et al.  A hybrid prognostics and health management approach for condition-based maintenance , 2009, 2009 IEEE International Conference on Industrial Engineering and Engineering Management.

[52]  M. Pecht,et al.  A fusion approach to IGBT power module prognostics , 2009, EuroSimE 2009 - 10th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems.

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

[54]  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).

[55]  K. Pattipati,et al.  Model-based Prognostic Techniques , 2003 .

[56]  Chao Liu,et al.  Study on impedance model of Li-ion battery , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.

[57]  Jun Lv,et al.  Degradation process prediction for rotational machinery based on hybrid intelligent model , 2012 .

[58]  Shaoping Wang,et al.  Prognostic analysis based on hybrid prediction method for axial piston pump , 2012, IEEE 10th International Conference on Industrial Informatics.

[59]  Matthew Daigle,et al.  Model-based prognostics under limited sensing , 2010, 2010 IEEE Aerospace Conference.

[60]  Kai Goebel,et al.  Prognostic Fusion for Uncertainty Reduction , 2007 .

[61]  Joseph Mathew,et al.  Intelligent condition-based prediction of machinery reliability , 2009 .

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

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

[64]  Peter Tavner,et al.  Reliability analysis for wind turbines with incomplete failure data collected from after the date of initial installation , 2009, Reliab. Eng. Syst. Saf..

[65]  P E Irving,et al.  Prognostic Control of Cracking in Structures and Components Operating in Hydrogen Environments , 2014 .

[66]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[67]  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.

[68]  W. Wang,et al.  A data-model-fusion prognostic framework for dynamic system state forecasting , 2012, Eng. Appl. Artif. Intell..

[69]  Samuel H. Huang,et al.  System health monitoring and prognostics — a review of current paradigms and practices , 2006 .

[70]  Stoyan Stoyanov,et al.  Prognostics framework for remaining life prediction of Cutty Sark iron structures , 2009 .

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