Prognostics: a literature review

Integrated systems health management (ISHM) is an enabling technology used to preserve safe and reliable operation of complex engineering systems. It also helps in reducing processing and operation time, manpower and cost, and increasing system availability and utility. ISHM includes various technologies ranging from design, analysis, build, and verify to operate and maintain. Prognostics is one of the most challenging and beneficial aspects of ISHM. Knowledge of the remaining useful life using prognostics can make a significant paradigm shift in ISHM. Researchers that have new interest in prognostics need to read hundreds of articles to have a complete picture about prognostics and its relation to other disciplines. Our contribution to solving this problem is by introducing the first comprehensive vision about prognostics as a part of ISHM in a single literature review paper. We focus on prognostics benefits, approaches, applications, and challenges. This paper can be considered as the starting point for studying prognostics and health management.

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[3]  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.

[4]  T. Dabney,et al.  PHM a key enabler for the JSF autonomic logistics support concept , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[5]  Puqiang Zhang,et al.  Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery , 2014 .

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

[7]  Scott Poll,et al.  A Survey of Health Management User Objectives in Aerospace Systems Related to Diagnostic and Prognostic Metrics , 2021 .

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

[9]  Michael Pecht,et al.  Prognostics and Health Management , 2013 .

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[19]  Byeng D. Youn,et al.  A generic probabilistic framework for structural health prognostics and uncertainty management , 2012 .

[20]  P.P. Bonissone,et al.  Domain Knowledge and Decision Time: A Framework for Soft Computing Applications , 2006, 2006 International Symposium on Evolving Fuzzy Systems.

[21]  Tom Gorka,et al.  Method for estimating capacity and predicting remaining useful life of lithium-ion battery , 2014, 2014 International Conference on Prognostics and Health Management.

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

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

[29]  Martin S. Feather,et al.  Guiding Technology Deployment Decisions using a Quantitative Requirements Analysis Technique , 2008, 2008 16th IEEE International Requirements Engineering Conference.

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

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

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

[33]  Zou Dan-ping.  Open System Architecture for Condition-Based Maintenance , 2012 .

[34]  G. Vachtsevanos,et al.  Reasoning about uncertainty in prognosis: a confidence prediction neural network approach , 2005, NAFIPS 2005 - 2005 Annual Meeting of the North American Fuzzy Information Processing Society.

[35]  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.

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

[37]  N. Iyer,et al.  Framework for post-prognostic decision support , 2006, 2006 IEEE Aerospace Conference.

[38]  Gautam Biswas,et al.  Integrated systems health management to achieve autonomy in complex systems , 2006 .

[39]  Sankalita Saha,et al.  Metrics for Offline Evaluation of Prognostic Performance , 2021, International Journal of Prognostics and Health Management.

[40]  Enrico Zio,et al.  A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system , 2010, Reliab. Eng. Syst. Saf..