Vibration signal reconstruction method for mechanical fault diagnosis

Abstract

The invention provides a vibration signal reconstruction method for mechanical fault diagnosis. In a non negative condition, sparser coding is carried out on original vibration signals, the signal spectrum detection effectiveness is improved, and the method belongs to the technical field of vibration signal processing. The method comprises steps: 1, vibration signals of a mechanical system are sampled, and the original vibration signals are overlapped and segmented to obtain column vectors to form a training sample; 2, a learning dictionary is initialized, sparse coding is carried out on the training sample based on a non negative-base tracking algorithm to obtain an initial sparse coding matrix; 3, the sparse coding matrix is fixed, and the learning dictionary is optimized and updated based on a non negative K singular value decomposition algorithm; 4, the learning dictionary is fixed, and sparse coding is carried out based on the non negative-base tracking algorithm to update a corresponding sparse coding matrix; 5, the former two steps are circulated, alternate iterative updating is carried out to obtain the final learning dictionary and the final sparse coding matrix; 6, an updating sample is calculated, and reconstructed signals are inversely overlapped; and 7, an envelope spectrum for the reconstructed signals is extracted for fault diagnosis. The method of the invention is used for mechanical fault diagnosis.

Description

technical field [0001] The invention belongs to the technical field of vibration signal processing, and mainly relates to the processing technology of mechanical equipment vibration signals. Background technique [0002] Obtaining the operating status information of mechanical equipment is the premise of system fault diagnosis. Due to the real-time nature and periodicity of the vibration signal itself and the small size of the acceleration sensor, wide frequency band, easy installation, and stable high-frequency response, the vibration signal has become the most widely used information carrier in the current mechanical system fault diagnosis. For the key components of rotating machinery, the method of fault diagnosis based on vibration signal spectrum detection is widely used. In the diagnosis process, vibration signal filtering and denoising (preprocessing) is the key, and it is also a research hotspot and difficulty in the field of signal processing. Its effectiveness dir...

AI Technical Summary

Problems solved by technology

Commonly used denoising methods are usually based on wavelet transform, empirical mode decomposition, spectral kurtosis, blind source separation, autoregressive model and other algorithms, all of which have achieved good results, but there are also limitations such as over-reliance on empirical knowledge and manual determination of parameters.

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