Remaining useful life estimation – A review on the statistical data driven approaches

• Published in: European journal of operational research Vol. 213; no. 1; pp. 1 - 14
• Main Authors: Si, Xiao-Sheng, Wang, Wenbin, Hu, Chang-Hua, Zhou, Dong-Hua
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 16.08.2011; Elsevier; Elsevier Sequoia S.A
• Series: European Journal of Operational Research
• Subjects: Applied sciences; Brown motion; Distribution theory; Estimates; Estimating; Estimating techniques; Exact sciences and technology; Health; Industrial metrology. Testing; Information; Linear inference, regression; Maintenance; Maintenance Remaining useful life Brown motion Stochastic filtering Proportional hazards model Markov; Markov; Mathematical models; Mathematics; Mechanical engineering. Machine design; Operational research; Operations research; Probability and statistics; Proportional hazards model; Remaining useful life; Sciences and techniques of general use; Statistical analysis; Statistical methods; Statistics; Stochastic filtering; Studies; Useful life; Maintenance; Stochastic filtering; Remaining useful life; Markov; Brown motion; Proportional hazards model; Markov process; Probabilistic approach; Statistical distribution; Health; Durability; Data driven modelling; Modeling; Cox model; Information source; Filtering theory; System identification; Monitoring
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2010.11.018

Remaining useful life (RUL) is the useful life left on an asset at a particular time of operation. Its estimation is central to condition based maintenance and prognostics and health management. RUL is typically random and unknown, and as such it must be estimated from available sources of information such as the information obtained in condition and health monitoring. The research on how to best estimate the RUL has gained popularity recently due to the rapid advances in condition and health monitoring techniques. However, due to its complicated relationship with observable health information, there is no such best approach which can be used universally to achieve the best estimate. As such this paper reviews the recent modeling developments for estimating the RUL. The review is centred on statistical data driven approaches which rely only on available past observed data and statistical models. The approaches are classified into two broad types of models, that is, models that rely on directly observed state information of the asset, and those do not. We systematically review the models and approaches reported in the literature and finally highlight future research challenges.