Mechanical fault diagnosis and signal feature extraction based on fuzzy neural network

To improve the limitation of applying traditional fault diagnosis method to the diagnosis of multi-concurrent vibrant faults of aeroengine, a new diagnosis approach combining the wavelet transform with fuzzy theory is proposed. A novel method based on the statistic rule is brought forward to determine the threshold of each order of wavelet space and the decomposition level adaptively, increasing the signal-noise-ratio. The effective eigenvectors are acquired by binary discrete wavelet transform and the fault modes are classified by fuzzy diagnosis equation based on correlation matrix. The fault diagnosis model of aeroengine is established and the extended Kalman filter (EKF) algorithm is used to fulfill the network structure and the robustness of fault diagnosis equation is discussed. By means of choosing enough samples to train the fault diagnosis equation and the information representing the faults is input into the trained diagnosis equation, and according to the output result the type of fault can be determined. Actual applications show that the proposed method can effectively diagnose multi-concurrent fault for aeroengine vibration and the diagnosis result is correct.