A review on prognostic techniques for non-stationary and non-linear rotating systems

被引:289
作者
Kan, Man Shan [1 ]
Tan, Andy C. C. [1 ]
Mathew, Joseph [1 ]
机构
[1] Queensland Univ Technol, Fac Sci & Engn, Chem Phys & Mech Engn Sch, Brisbane, Qld 4000, Australia
关键词
Prognostics; Non-stationary; Non-linear; Rotating systems; SEMI-MARKOV MODEL; FATIGUE-CRACK PROPAGATION; SUPPORT VECTOR MACHINES; EXTENDED KALMAN FILTER; REMAINING USEFUL LIFE; ADAPTIVE NEURO-FUZZY; SELF-ORGANIZING MAP; GAUSSIAN-PROCESSES; PARTICLE FILTER; CONDITION PREDICTION;
D O I
10.1016/j.ymssp.2015.02.016
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The field of prognostics has attracted significant interest from the research community in recent times. Prognostics enables the prediction of failures in machines resulting in benefits to plant operators such as shorter downtimes, higher operation reliability, reduced operations and maintenance cost, and more effective maintenance and logistics planning. Prognostic systems have been successfully deployed for the monitoring of relatively simple rotating machines. However, machines and associated systems today are increasingly complex. As such, there is an urgent need to develop prognostic techniques for such complex systems operating in the real world. This review paper focuses on prognostic techniques that can be applied to rotating machinery operating under non-linear and non-stationary conditions. The general concept of these techniques, the pros and cons of applying these methods, as well as their applications in the research field are discussed. Finally, the opportunities and challenges in implementing prognostic systems and developing effective techniques for monitoring machines operating under non-stationary and non-linear conditions are also discussed. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1 / 20
页数:20
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