Fault Diagnosis for Engine Based on EMD and Wavelet Packet BP Neural Network

To solve the problem of fault diagnosis for engine, due to the complexity of the equipments and the particularity of the operating environments, generally speaking, there is no one-to-one correspondence between the characteristic parameters and status, so, the methods of diagnosis are very complicated. A novel fault diagnosis method based on empirical mode decomposition (EMD) and wavelet packet BP neural network is proposed in this paper. Firstly, the given signal is analyzed by wavelet packet to remove the noise; Then the de-noised data is decomposed into a number of IMFs by EMD and extract their frequency eigenvectors, then using these eigenvectors as the training samples of the BP network, training the BP network to identify the faults. Finally, the simulation experiments shows that the proposed method for fault diagnosis of engine is effective and the de-nosing process using wavelet packet transform is essential.