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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