Fault bearing diagnosis method based on SVD and CEEMDAN

Abstract

The invention relates to a fault bearing diagnosis method based on SVD and CEEMDAN. The method comprises the following steps: collecting a bearing vibration signal; calculating time-frequency distribution, and preliminarily judging whether the bearing has a fault or not; carrying out singular value decomposition denoising reconstruction on the original fault bearing signal; performing adaptive noise complete set empirical mode decomposition on the preliminary noise reduction signal to obtain a plurality of intrinsic mode components; calculating the KL divergence between each intrinsic mode component and the original signal, and removing invalid components; reconstructing the effective intrinsic mode component; and carrying out self-correlation denoising on the reconstructed signal, drawing an envelope spectrum, extracting a clear fault characteristic frequency, and diagnosing the fault type of the bearing. Reconstruction is carried out after singular value decomposition denoising, modal decomposition is carried out, invalid components are removed, fault diagnosis is carried out by using an envelope spectrum of a reconstructed signal, and the accuracy of bearing fault diagnosis is effectively improved; and, modal aliasing is eliminated, consumption of computing resources is greatly reduced, and signal decomposition efficiency is improved.

Description

technical field [0001] The invention belongs to the field of mechanical fault diagnosis, in particular to a fault bearing diagnosis method based on SVD and CEEMDAN. Background technique [0002] At present, fault diagnosis technology has become an important means to ensure the safe and reliable operation of mechanical equipment. Rolling bearings are important components in rotating machinery. When they fail, they will not only generate vibration and noise, but also affect the operating efficiency of the rotating machinery. If the bearing failure cannot be found in time, it may cause major safety accidents and economic losses. Therefore, it is necessary to carry out research on fault diagnosis of rolling bearings. When a rolling bearing fails, its measured signal is accompanied by noise and fault signals. How to extract these fault signals is of great significance in actual industrial production. [0003] In recent years, in order to improve the quality of signal processin...

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Problems solved by technology

Commonly used methods such as empirical mode decomposition (empirical mode decomposition, EMD), ensemble empirical mode decomposition (Ensemble Empirical Mode Decomposition, EEMD), complete ensemble mode decomposition (Complementary Ensemble Empirical Mode Decomposition, CEEMD) and other decomposition methods have modal Aliasing problem

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