Artificial intelligence for fault diagnosis of rotating machinery: A review

Wavelets for fault diagnosis of rotary machines: A review with applications

Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

Artificial Neural Networks (ANNs)

New clustering algorithm-based fault diagnosis using compensation distance evaluation technique

Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms

This paper presents a multi-stage algorithm for the dynamic condition monitoring of a gear. The algorithm provides information referred to the gear status (fault or normal condition) and estimates the mesh stiffness per shaft revolution in case that any abnormality is detected. In the first stage, the analysis of coefficients generated through discrete wavelet transformation (DWT) is proposed as a fault detection and localization tool. The second stage consists in establishing the mesh stiffness reduction associated with local failures by applying a supervised learning mode and coupled with analytical models. To do this, a multi-layer perceptron neural network has been configured using as input features statistical parameters sensitive to torsional stiffness decrease and derived from wavelet transforms of the response signal. The proposed method is applied to the gear condition monitoring and results show that it can update the mesh dynamic properties of the gear on line.

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Javier Sanz

Papers

Fault diagnosis of rotating machinery based on auto-associative neural networks and wavelet transforms

Gear dynamics monitoring using discrete wavelet transformation and multi-layer perceptron neural networks