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

Abstract

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.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of variable working conditions of bearings, in particular to an adaptive fault diagnosis method based on permutation entropy (PE) and manifold-based dynamic time warping (MDTW). Background technique [0002] Bearings are widely used in rotating machinery, and their health status directly affects the normal operation of the entire rotating machinery, which in turn affects the entire system. In recent years, bearing fault diagnosis has become a research hotspot, especially the fault diagnosis based on vibration signals, and there are many effective methods at present. The process of bearing fault diagnosis mainly includes two aspects: fault feature extraction and fault state determination. The method of the invention aims to make the bearing fault diagnosis process more systematic, efficient and easy to operate, and to ensure better real-time characteristics. [0003] For how to extract ef...

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Problems solved by technology

However, in the DTW algorithm, the similarity measure is based on the square of the Euclidean distance, which cannot guarantee the separability between small data samples, let alone reflect the global consistency of the data.

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