Remaining useful life prediction of rotating equipment using covariate-based hazard models – Industry applications

Abstract

The ability to estimate the expected remaining useful life (RUL) is critical to reduce maintenance costs, operational downtime and safety hazards. In most industries, reliability analysis is based on the reliability-centred maintenance and lifetime distribution models. In these models, the lifetime of an asset is estimated using failure time data; however, statistically sufficient failure time data are often difficult to attain in practice due to the fixed time-based replacement and the small population of identical assets. When condition indicator data are available in addition to failure time data, one of the alternate approaches to the traditional reliability models is the condition-based maintenance (CBM). The covariate-based hazard modelling is one of CBM approaches. There are a number of covariate-based hazard models; however, little study has been conducted to evaluate the performance of these models in asset life prediction using various condition indicators and data availability. This paper reviews two covariate-based hazard models, proportional hazard model (PHM) and proportional covariate model (PCM). To assess these models’ performance, the expected RUL is compared to the actual RUL. Outcomes demonstrate that both models achieve convincingly good results in RUL prediction; however, PCM has smaller absolute prediction error. In addition, PHM shows over-smoothing tendency compared to PCM in sudden changes of condition data. Moreover, the case studies show PCM is not being biased in the case of small sample size.

Keywords:

• Hazard function
• reliability
• remaining useful life
• proportional hazard model
• proportional covariate model

Acknowledgements

The authors are grateful to the Centre for Maintenance Optimization and Reliability Engineering (C-MORE) at University of Toronto for generously providing the pulp mill data used to develop this work. Also thanks to Dr Hack-Eun Kim for kindly providing LNG data for this research paper.

Disclosure statement

No potential conflict of interest was reported by the authors.