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Content Provider | IEEE Xplore Digital Library |
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Author | Sadeghi, M.H. Rafiee, J. Arvani, F. Harifi, A. |
Copyright Year | 2005 |
Description | Author affiliation: Center of Excellence for Mechatronics, Tabriz Univ. (Sadeghi, M.H.; Rafiee, J.; Arvani, F.; Harifi, A.) |
Abstract | This paper concentrates on a new procedure which experimentally recognizes gears and bearings faults of a typical gearbox system using a multi-layer perceptron neural network. Feature vector which is one of the most significant parameters to design an appropriate neural network was innovated by standard deviation of wavelet packet coefficients. The gear conditions were considered to be normal gearbox and slight- and medium-worn and broken-teeth gears faults and a general bearing fault which were five neurons of output layer with the aim of fault detection and identification. A downscaled 2-layer multi-layer perceptron neural-network-based system with great accuracy was designed to carry out the task. Vibration signals were recognized as the most reliable source to extract the feature vector which were by piecewise cubic Hermite interpolation synchronized and pre-processed using the standard deviation of wavelet packet coefficients in this research |
Starting Page | 964 |
Ending Page | 969 |
File Size | 2961067 |
Page Count | 6 |
File Format | |
ISBN | 0780394224 |
DOI | 10.1109/ICNNB.2005.1614780 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2005-10-13 |
Publisher Place | China |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Neurons Fault Diagnosis Multi-layer neural network Fault diagnosis Wavelet Interpolation Neural Network Gears Fault detection Neural networks Multilayer perceptrons Feature extraction Wavelet packets Non-destructive testing Gearbox |
Content Type | Text |
Resource Type | Article |
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