A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximation theory and feedforward networks
Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Inverse Problem Theory and Methods for Model Parameter Estimation
Inverse Problem Theory and Methods for Model Parameter Estimation
Feature-based classifier ensembles for diagnosing multiple faults in rotating machinery
Applied Soft Computing
Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)
Applied Soft Computing
An expansion of a signal in Gaussian elementary signals (Corresp.)
IEEE Transactions on Information Theory
Hi-index | 0.00 |
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.