Intelligent Built-in Test Fault Diagnosis Based on Wavelet Analysis and Neural Networks

This paper proposes a new intelligent built-in test (BIT) fault diagnosis system based on wavelet analysis and neural networks. The aim of this investigation is to improve the fault diagnosis capability of intelligent BIT for More-Electric Aircraft Electrical Power System (MEAEPS). In constructing the BIT system, the wavelet packet transform is applied to extract fault features. Through the wavelet packet decomposition, we get the fault eigenvectors and input them into a hybrid neural network, which performs in the role of a fault classifier. This hybrid network adds a supervised learning vector quantization (LVQ) layer to the generalized learning vector quantization (GLVQ) network, which makes the boundaries among the fault classes more discriminative than using the GLVQ network alone. Since the original GLVQ algorithm suffers from several major problems, we modify the original algorithm in order to make this network more suitable for application. This modified algorithm employs a new form of loss factor, and its learning rules are derived through finding a minimum of the loss function. Finally, the proposed method has been applied to the BIT system of the MEAEPS, and the results have shown that the proposed method is promising to improve the performance of the intelligent BIT system