A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized regression neural network in modelling river sediment yield
Advances in Engineering Software
A Fast Fourier Transform for High-Speed Signal Processing
IEEE Transactions on Computers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A general regression neural network
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Application of multiclass support vector machines for fault diagnosis of field air defense gun
Expert Systems with Applications: An International Journal
Intelligent fault inference for rotating flexible rotors using Bayesian belief network
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Journal of Intelligent Manufacturing
Hi-index | 12.06 |
This paper describes a fault diagnosis system for automotive generators using discrete wavelet transform (DWT) and an artificial neural network. Conventional fault indications of automotive generators generally use an indicator to inform the driver when the charging system is malfunction. But this charge indicator tells only if the generator is normal or in a fault condition. In the present study, an automotive generator fault diagnosis system is developed and proposed for fault classification of different fault conditions. The proposed system consists of feature extraction using discrete wavelet analysis to reduce complexity of the feature vectors together with classification using the artificial neural network technique. In the output signal classification, both the back-propagation neural network (BPNN) and generalized regression neural network (GRNN) are used to classify and compare the synthetic fault types in an experimental engine platform. The experimental results indicate that the proposed fault diagnosis is effective and can be used for automotive generators of various engine operating conditions.