A review on prognostic techniques for non-stationary and non-linear rotating systems
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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.
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|Item Type:||Journal Article|
|Keywords:||Prognostics;, Non-stationary, Non-linear, Rotating systems|
|Divisions:||Current > Schools > School of Chemistry, Physics & Mechanical Engineering
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2015 Elsevier|
|Copyright Statement:||This is the author’s version of a work that was accepted for publication in Mechanical Systems and Signal Processing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Mechanical Systems and Signal Processing, [VOL 62-63, 2015] DOI: 10.1016/j.ymssp.2015.02.016|
|Deposited On:||21 May 2015 22:53|
|Last Modified:||03 Jun 2015 04:11|
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