Multidimensional prognostics for rotating machinery: A review

Abstract

Determining prognosis for rotating machinery could potentially reduce maintenance costs and improve safety and avail- ability. Complex rotating machines are usually equipped with multiple sensors, which enable the development of multidi- mensional prognostic models. By considering the possible synergy among different sensor signals, multivariate models may provide more accurate prognosis than those using single-source information. Consequently, numerous research papers focusing on the theoretical considerations and practical implementations of multivariate prognostic models have been published in the last decade. However, only a limited number of review papers have been written on the subject. This article focuses on multidimensional prognostic models that have been applied to predict the failures of rotating machinery with multiple sensors. The theory and basic functioning of these techniques, their relative merits and draw- backs and how these models have been used to predict the remnant life of a machine are discussed in detail. Furthermore, this article summarizes the rotating machines to which these models have been applied and discusses future research challenges. The authors also provide seven evaluation criteria that can be used to compare the reviewed techniques. By reviewing the models reported in the literature, this article provides a guide for researchers considering prognosis options for multi-sensor rotating equipment.

Description

open access article

Keywords

Prognosis, rotating machinery, condition monitoring, multivariate models, prognostics and health management

Citation

Li, X., Duan, F., Mba, D. and Bennett, I. (2017) Multidimensional prognostics for rotating machinery: A review. Advances in Mechanical Engineering, 9(2), p.1687814016685004.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)