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.
Impact and interest:
Citation counts are sourced monthly from and citation databases.
These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.
|Item Type:||Conference Paper|
|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:||Current > Research Centres > CRC Integrated Engineering Asset Management (CIEAM)
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Schools > School of Engineering Systems
|Copyright Owner:||Copyright 2002 (please consult author)|
|Deposited On:||17 Feb 2009 04:55|
|Last Modified:||09 Jun 2010 13:23|
Repository Staff Only: item control page