Fault diagnosis method for rolling bearing based on Bayesian residual transform, singular value decomposition and Gaussian mixture hidden Markov model framework

Abstract

The invention belongs to the field of mechanical equipment fault diagnosis, and discloses a fault diagnosis method for a rolling bearing based on Bayesian residual transform, singular value decomposition and Gaussian mixture hidden Markov model framework. The fault diagnosis method specifically comprises the steps that firstly a fault bearing signal is acquired; an acceleration sensor fixed on a tested bearing cover amplifies the signal and then transmits the amplified signal to a multi-channel data acquisition analyzer, and the analyzer sends the acquired signal to a PC; secondly, the acquired signal is subjected to the following processing: step one, the signal is decomposed through Bayesian residual transform, noise elimination and reconstruction are performed on the residual signal, the reconstructed signal contains clearer signal features; step two, a singular value vector of the reconstructed signal acquired in the previous step is extracted by adopting singular value decomposition, and the stability of fault features is improved; and step three, classification is performed on bearing faults according to a Gaussian mixture hidden Markov model. The fault diagnosis method has the advantage of accurate diagnosis for a fault of the rolling bearing.

Description

technical field [0001] The invention belongs to the technical field of mechanical equipment fault diagnosis, in particular to a rolling bearing fault diagnosis method based on Bayesian residual transformation-singular value decomposition-Gaussian mixed hidden Markov model framework. Background technique [0002] Bearings are widely used in modern mechanical equipment and are an indispensable part of rotating machinery. Its main function is to support the mechanical rotating body, reduce the friction coefficient during its movement, and ensure its rotation accuracy. Due to harsh working conditions such as high speed and heavy load, the life of the bearing becomes shorter, which causes various mechanical failures. These failures can lead to damage to machines, ranging from financial loss to catastrophic accidents. [0003] As a new discipline, mechanical equipment fault diagnosis technology was first developed in the United States in the 1960s. my country's fault diagnosis ...

AI Technical Summary

Problems solved by technology

However, these diagnostic methods have inevitable defects.

For example, the transform scale adopted by the wavelet transform method is small, when there is strong energy interference in the low frequency band, the effective value of the feature quantity will decrease, resulting in unsatisfactory diagnosis results

Empirical mode decomposition is difficult to avoid the endpoint effect, that is, the upper and lower envelopes diverge at both ends of the data sequence, and this divergence will gradually inward as the operation proceeds, thus affecting the entire data sequence

[0006] Wigner-Ville distribution has good time-frequency aggregation, but for multi-component signals, according to the convolution theorem, there will be cross terms, resulting in "false signals", and defects in the diagnosis process

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