Fuzzy Sets and Systems
Classifying inventory using an artificial neural network approach
Computers and Industrial Engineering
Classification of heart sounds using an artificial neural network
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Adaptive probabilistic neural networks for pattern classification in time-varying environment
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
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.