Bearing Fault Classification and Diagnosis Method Based on Sparse Representation and Integrated Learning

Abstract

The present invention discloses a bearing fault classification diagnosis method based on sparse representation and ensemble learning. The method comprises the steps of acquiring the vibration acceleration signals of a rolling bearing at different working rotating speeds via an acceleration sensor under each working condition as the training samples; selecting m training samples to form m sets of training data, establishing a weak classifier-graph regularization sparse representation model, and carrying out T times iterative operation on the graph regularization sparse representation; obtaining a classification function sequence via a weak classifier, then giving a weight to each classification function, and finally obtaining a strong classification function F by weighting a weak classification function; acquiring the vibration acceleration signal data of the to-be-tested rolling bearing at the rotation work via the acceleration sensor as a test sample; taking the test sample as the input quantity of the strong classification function to introduce in the strong classification function to operate, thereby being able to obtain a fault classification result of the to-be-tested rolling bearing. The method of the present invention enables the accuracy and the validity of the rolling bearing fault diagnosis to be improved.

Description

technical field [0001] The invention relates to bearing mechanical fault diagnosis, in particular to a bearing fault classification diagnosis method based on sparse representation and integrated learning, which belongs to the technical fields of mechanical fault diagnosis and computer artificial intelligence. Background technique [0002] Bearing is one of the most widely used components in mechanical equipment, widely used in various important departments such as chemical industry, metallurgy, electric power, aviation, etc., and it is also one of the most vulnerable components. With the increasing precision and complexity of mechanical equipment, the precision and reliability requirements for rolling bearings are also getting higher and higher. Bearing damage, even minor damage, may affect the realization of the normal function of the entire equipment. Therefore, the extraction of rolling bearing fault information, bearing fault diagnosis, especially the analysis of early ...

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Problems solved by technology

However, in most cases, the spectrum analysis method requires manual judgment, and the obtained spectrum is sometimes very complicated and difficult to recognize intuitively, requiring the analyst to have rich analysis experience

[0005] In short, there are certain defects in the existing mechanical fault diagnosis methods, and it is difficult to accurately identify and diagnose bearing faults by using the existing mechanical fault diagnosis methods.

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