A Bearing Fault Diagnosis Method Based on Walsh Transform and Teager Energy Operator

Abstract

The invention belongs to the field of mechanical equipment fault diagnosis. The invention discloses a rolling bearing fault diagnosis method based on the Walsh transformation and Teager energy operator. The specific contents are as follows: first, an acceleration sensor is fixed on a bearing seat under test, the acceleration sensor is connected with a data acquisition instrument to obtain a bearing vibration signal, then a bearing fault diagnosis is performed, and subjecting the signal to Walsh transform denoising; fault features are extracted by Teager energy operator demodulation; and the bearing fault type is positioned and identified by comparing with a theoretical calculating value. The invention takes the vibration signal as the research object, combines the Walsh transform denoisingand the Teager energy operator demodulation, and proposes the novel bearing fault diagnosis method. The bearing fault diagnosis method in the invention can effectively diagnose the fault type of a rolling bearing and has the advantages of high diagnostic accuracy.

Description

technical field [0001] The invention belongs to the technical field of mechanical equipment fault diagnosis, in particular to a bearing fault diagnosis method based on Walsh transformation and Teager energy operator. Background technique [0002] Under modern industrial production conditions, the structure of mechanical equipment is becoming more and more refined, the level of automation is getting higher and higher, the operating conditions are becoming more and more complex, and people's requirements for mechanical equipment are becoming more and more stringent. Rolling bearing is an important part of the machine, it is widely used in chemical industry, metallurgy, electric power, aerospace and other important departments. [0003] Rolling bearings are also one of the most easily damaged components. According to statistics, bearing failures account for as much as 40% of rotating machinery failures. The main measure to deal with bearing failures at this stage is to carry o...

AI Technical Summary

Problems solved by technology

However, the use of wavelet transform to suppress noise often causes oscillation effects when dealing with low signal-to-noise ratio signals. At the same time, it is necessary to manually select a suitable wavelet when performing wavelet transform, which greatly increases the time required for the processing of practical engineering problems.

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