An engine fault diagnosis system using intake manifold pressure signal and WignerVille distribution technique
This paper proposed an engine fault diagnosis system based on intake manifold pressure signal and artificial neural network with the WignerVille distribution technique. Traditionally, the engine diagnostic method depends on the experience of the technician, but some faults might be inaccurately judged by the technicians experience when the engine is operating. In the present study, an engine platform diagnosis system using intake manifold pressure was developed. The algorithm of the proposed system consisted of WignerVille distribution (WVD) for feature extraction and the neural network technique for fault classification. In previous work, the WignerVille distribution was often used to analyze the non-stationary signal, because it provides a simple and clear energy spectrum diagram both in the time and frequency domains. This instantaneous energy diagram presented the magnitude of each engine fault under various operating conditions. The WignerVille distribution extracts these features as database input to a neural network and the neural network is used to develop the training and testing modules. To prove the efficiency of the neural network, both the radial basis function neural network and generalized regression neural network are used and compared. The experimental results demonstrated the proposed system is effective and the performance is satisfactory.
fault diagnosis system intake manifold pressure wignerville distribution artificial neural network