Intelligent diagnosis of rotating machinery faults - A review

Yang, Hongyu, Mathew, Joseph, & Ma, Lin (2002) Intelligent diagnosis of rotating machinery faults - A review. In 3rd Asia-Pacific Conference on Systems Integrity and Maintenance, ACSIM 2002, 25-27 September 2002, Cairns, Australia.


The task of condition monitoring and fault diagnosis of rotating machinery faults is both significant and important but is often cumbersome and labour intensive. Automating the procedure of feature extraction, fault detection and identification has the advantage of reducing the reliance on experienced personnel with expert knowledge. Various diagnostics methods have been proposed for different types of rotating machinery. However, little research has been conducted on synthesizing and analysing these techniques, resulting in apprehension when technicians need to choose a technique suitable for application. This paper presents a review of a variety of diagnosis techniques that have had demonstrated success when applied to rotating machinery and highlights fault detection and identification techniques based mainly on artificial intelligence approaches. The literature is categorised in the following diagnostic groups: neural networks, fuzzy sets, expert systems, and hybrid AI techniques based fault diagnosis. The paper concludes with a brief description of a new approach to diagnosis using a Wavelet based Coactive Artificial Neuro-Fuzzy Inference System (CANFIS) which the authors plan to develop and implement for diagnosing machine faults.

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ID Code: 17942
Item Type: Conference Paper
Refereed: Yes
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > MECHANICAL ENGINEERING (091300)
Divisions: Past > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Copyright Owner: Copyright 2002 (please consult author)
Deposited On: 17 Feb 2009 04:55
Last Modified: 22 Jun 2017 14:41

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