Towards Enhanced Prognostics with Advanced Data-Driven Modelling

A considerable amount of prognostics research has been conducted to improve the remaining useful life prediction of engineering assets. Advantages such as lowering sustainment costs and improving maintenance decision making, are significant motivations to enhance the prognostics capability. Sensor selection, data pre-processing, knowledge elicitation and the mathematical techniques are some of the elements required of prognostics research to enhance capability. This paper takes a broad view of prognostics and explores techniques available from a variety of research and application disciplines. A prognostics dataflow diagram illustrates the complete prognostics process and the paper discusses the impact of improvements in each process step to enhance the prognostics performance. The mathematical approach to prognostics is a crucial issue. Exploring cross-disciplinary prognostic approaches is helpful to extract useful techniques from different domains and to fuse the strengths of each discipline. A case study of fatigue induced crack-growth using Bayesian approaches is used to illustrate that data-driven prognostics can deliver benefits to the industry.

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

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

[3]  Alyson G. Wilson,et al.  Regression Models in Reliability , 2008 .

[4]  W. D. Ray.  4. Modelling Survival Data in Medical Research , 1995 .

[5]  Kai Goebel,et al.  A Survey of Artificial Intelligence for Prognostics , 2007, AAAI Fall Symposium: Artificial Intelligence for Prognostics.

[6]  K.W. Przytula,et al.  Reasoning Framework for Diagnosis and Prognosis , 2007, 2007 IEEE Aerospace Conference.

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

[8]  Munir Ahmad,et al.  Bernstein reliability model: Derivation and estimation of parameters , 1984 .

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

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

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

[12]  D. Kleinbaum,et al.  Survival Analysis: A Self-Learning Text. , 1996 .

[13]  David Collett.  Modelling Survival Data in Medical Research , 1994 .

[14]  W. Marsden.  I and J , 2012 .

[15]  Jeff Harrison,et al.  Applied Bayesian Forecasting and Time Series Analysis , 1994 .

[16]  Donald A. Berry,et al.  Statistics: A Bayesian Perspective , 1995 .

[17]  Zhanshan Ma,et al.  Survival Analysis Approach to Reliability, Survivability and Prognostics and Health Management (PHM) , 2008, 2008 IEEE Aerospace Conference.

[18]  Peter Sandborn,et al.  A Methodology for Determining the Return on Investment Associated With Prognostics and Health Management , 2009, IEEE Transactions on Reliability.

[19]  Y Shao,et al.  Prognosis of remaining bearing life using neural networks , 2000 .

[20]  Rong Li,et al.  Residual-life distributions from component degradation signals: A Bayesian approach , 2005 .

[21]  Sankaran Mahadevan,et al.  Uncertainty quantification and model validation of fatigue crack growth prediction , 2011 .

[22]  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.

[23]  C.S. Byington,et al.  A model-based approach to prognostics and health management for flight control actuators , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[24]  Steven C. Wheelwright,et al.  Forecasting methods and applications. , 1979 .

[25]  Zhanshan Ma,et al.  Competing Risks Analysis of Reliability, Survivability, and Prognostics and Health Management (PHM) , 2008, 2008 IEEE Aerospace Conference.

[26]  J. Banks,et al.  Cost Benefit Analysis for Asset Health Management Technology , 2007, 2007 Annual Reliability and Maintainability Symposium.