Self-adaption fault diagnosis method based on permutation entropy (PE) and manifold-based dynamic time warping (MDTW)

The present invention discloses a self-adaption fault diagnosis method based on permutation entropy (PE) and manifold-based dynamic time warping (MDTW), enabling a bearing fault diagnosis process to be systematic and raising handleability and real-time performance of the diagnosis method. Firstly, a nonlinear and nonstationary bearing vibration signal is decomposed into a plurality of single-package components by applying an adaptive time-frequency analysis method; the adaptive time-frequency analysis method may be selected from empirical mode decomposition, local mean decomposition and local characteristic-scale decomposition methods; and then, extracting the PE of each single-package component as a fault signature. The PE can reflect complexity of the signal and has high robustness and rapidity. The MDTW method is provided by the present invention so as to rapidly and accurately measure distance test data and training data, thereby determining the current fault state and realizing bearing fault diagnosis, and the method has excellent practical engineering application values.