Loading...
|
Please use this identifier to cite or link to this item:
http://ir.ncue.edu.tw/ir/handle/987654321/18358
|
| Title: | Application of Wigner-Ville Distribution and Probability Neural Network for Scooter Engine Fault Diagnosis |
| Authors: | Wu, Jian-Da;Chiang, Peng-Hsin |
| Contributors: | 車輛科技研究所 |
| Keywords: | Fault diagnosis system;Feature extraction;Wigner–Ville distribution;Neural network;Probability neural network |
| Date: | 2009-03
|
| Issue Date: | 2014-04-29T07:28:10Z
|
| Publisher: | Elsevier Ltd |
| Abstract: | An expert system for internal combustion engine fault diagnosis using Wigner–Ville distribution for feature extraction and probability neural network for fault classification is described in this paper. Most of the conventional techniques for fault signal analysis in a mechanical system are based chiefly on the difference of signal amplitude in the time and frequency domains. Unfortunately, in some conditions the performance is limited, such as when analysis signals are non-stationary. In the present study, the Wigner–Ville distribution is proposed for sound emission signal features classification, because it provides high resolution of instantaneous energy density both in time and frequency domains. Meanwhile, the instantaneous power spectrum is presented to obtain high-energy density when the engine fault condition occurs. These features of signals are classified using the probability neural network. To examine the efficiency of the probability neural network, both back-propagation and radial basis function neural networks are used in comparison with fault classification. The experimental results showed all three networks can achieve high recognition rate with feature extraction using Wigner–Ville distribution method. It also suggested the probability neural network can complete training in an extremely short time. |
| Relation: | Expert Systems with Applications, 36(2)1: 2187-2199 |
| Appears in Collections: | [車輛科技研究所] 期刊論文
|
Files in This Item:
| File |
Size | Format | |
|---|
| index.html | 0Kb | HTML | 392 | View/Open |
|
All items in NCUEIR are protected by copyright, with all rights reserved.
|
