Rotating machinery fault diagnosis based on multiple fault manifolds

The existing fault diagnosis methods based on manifold learning assume that all the faults distribute on a single manifold, however the faults may distribute on different manifolds in practical applications. Aiming at this problem, rotating machinery fault diagnosis method based on multiple fault manifolds is proposed. Firstly, mixed-domain features are extracted from the vibration signals to characterize the property of the faults, and the vibration signals are also preprocessed by empirical model decomposition before feature extraction. Then, the corresponding fault manifold of each fault is extracted from the high-dimensional fault samples. In the method, linear local tangent space alignment is applied to solve the problem of low-dimensional manifold extraction, and immune genetic algorithm is used to select the intrinsic dimensionality of fault manifold. At last, the test samples are respectively projected to all the fault manifolds, and the projection errors are used as the criterion to determine the fault types of the test samples. In order to verify the effectiveness of the proposed fault diagnosis method, the method is applied to diagnose the faults of the gear box. The experimental results indicate that feature compression can remove the redundant information between features, and moreover fault diagnosis method based on multiple fault manifolds can obtain even better performance than those methods which project all the faults to a single low-dimensional manifold.