Diagnosis method of rolling bearing fault

Abstract

The invention discloses a diagnosis method of a rolling bearing fault. The method comprises the following steps of S1, collecting an acceleration signal of a rolling bearing; S2, using a discrete wavelet transform method and a soft threshold method in a combination mode and carrying out de-noising processing on the acceleration signal; S3, carrying out segmentation on a time sequence of the acceleration signal after the de-noising processing and extracting a sample; S4, through more than two self-encoding networks, constructing a stack self-encoding network framework, and extracting characteristic information of the sample; S5, using the characteristic information of the sample to train at least one BP nerve network classifier; and S6, according to a fault diagnosis model established through using known fault data to train the at least one BP nerve network classifier, determining fault information of the rolling bearing. By using the diagnosis method of the rolling bearing fault, accuracy of the fault diagnosis is greatly increased.

Description

technical field [0001] The invention relates to the field of mechanical automation, in particular to a method for diagnosing rolling bearing faults. Background technique [0002] Rolling bearings have always been the core components of mechanical equipment, and they are also one of the most prone to failure components; once a rolling bearing fails, it will have an adverse effect on the entire mechanical equipment. Therefore, designing an accurate and efficient rolling bearing fault diagnosis method, quickly and accurately diagnosing the location and magnitude of the bearing fault, and adopting an effective treatment method for the fault is of great significance for ensuring the normal and safe operation of mechanical equipment. [0003] At present, there are many diagnostic methods for rolling bearing faults, which can be mainly divided into methods based on acoustic signal processing, vibration signal detection, acceleration signal processing, etc. The effects of these meth...

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Problems solved by technology

[0003] At present, there are many diagnostic methods for rolling bearing faults, which can be mainly divided into methods based on acoustic signal processing, vibration signal detection, acceleration signal processing, etc. The effects of these methods largely depend on the preprocessing of the collected real data. capability and feature extraction capability; however, the limited processing capability of the collected data in the prior art leads to a great reduction in the accuracy of the final fault diagnosis result

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