Missing vibration signal recovery method based on variational Bayesian parallel factorization

Abstract

The invention discloses a missing vibration signal recovery method based on variational Bayesian parallel factorization, which comprises the following steps of: constructing a three-dimensional tensor by a collected time domain vibration signal through segmenting a sampling point, introducing a likelihood model by combining the decomposed three-dimensional tensor with a Bayesian method, effectively utilizing the prior information of a factor matrix, introducing posteriori distribution with effective precision, processing a model by adopting a Bayesian method, deducing posteriori distribution of parameters of all unknown numbers including a factor matrix and a hyper-parameter, deducing posteriori distribution of the factor matrix and the hyper-parameter by adopting a variational Bayesian algorithm, and further deducing distribution prediction of missing signals. The performance of the method is evaluated by using a root-mean-square error, compared with a traditional low-rank tensor completion algorithm, the variational Bayesian parallel factorization algorithm has the advantages that the error is smaller, missing signals can be more effectively recovered, and the problem of signal missing caused by sensor failure in vibration signal analysis is effectively solved.

Description

technical field [0001] The invention relates to signal processing technology, in particular to a method for recovering missing vibration signals based on variational Bayesian parallel factorization. Background technique [0002] Frequently used components in mechanical equipment are prone to potential failures under harsh working environments such as high temperature, heavy load, and long-term online use. And equipment failure will affect production efficiency, leading to serious production, economic loss and even personal injury. Therefore, it is of high practical value to make accurate analysis of the faulty parts and realize the accurate diagnosis and identification of mechanical faults. At present, in the field of vibration signal analysis, various vibration signal analysis methods have been proposed because of their dynamic information analysis capabilities. For example: wavelet transform (WT) [1], empirical mode decomposition (EMD) [2], variational mode decomposition...

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Problems solved by technology

[0006] In order to solve the problem of sensor failure and lack of signal acquisition due to human factors and irresistible factors in nature, the present invention provides a recovery method for missing vibration signals based on variational Bayesian parallel factorization , infer the distribution prediction of missing elements, solve the missing signal values ​​that have always existed in the field of vibration analysis, and better solve the problem of signal loss

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