A review on prognostic techniques for non-stationary and non-linear rotating systems. (October 2015)

• Record Type: Journal Article
• Authors: Kan, Man Shan; Tan, Andy C.C.; Mathew, Joseph
• Is Part Of: Mechanical systems and signal processing. Volume 62/63(2015)
• Page Start: 1
• Page End: 20
• Publication Date: 2015-10
• Subjects: Prognostics -- Non-stationary -- Non-linear -- Rotating systems; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2015.02.016
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: 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. Highlights: Review of prognostic techniques for non-stationary and/or nonlinear systems. Prognostic techniques considering resource requirements. Standard scoring system to compare performance of prognostic techniques. Prognostic validation techniques with limited historical data. Proper selection of prediction horizons to suit different machines.