Motor rolling bearing partial pitting fault diagnosis method and diagnosis system thereof

Abstract

The invention discloses a motor rolling bearing partial pitting fault diagnosis method and a diagnosis system thereof. The motor rolling bearing partial pitting fault diagnosis method comprises the following steps: arranging a measuring point at an end cover of a bearing seat of the motor rolling bearing, wherein a vibration acceleration sensor collects a vibration signal of the measuring point; generating a kurtosis distribution diaphragm based on the vibration signal, determining filter center frequency and frequency band width corresponding to the maximum kurtosis value; performing filtering processing on the vibration signal based on the filter center frequency and frequency band width so as to obtain the filtered characteristic enhancement signal; performing cyclostationary analysis based on the characteristic enhancement signal so as to generate a cyclic spectrum density map; detecting whether the bearing fault characteristic frequency is existent in a cyclic frequency domain ofthe cyclic spectrum density map, and determining the vibration frequency of the fault impact waveform based on the cyclic frequency domain, thereby diagnosing the motor rolling bearing pitting fault.

Description

technical field [0001] The invention belongs to the technical field of rolling bearing fault diagnosis, and in particular relates to a local pitting fault diagnosis method and a diagnosis system of a rolling bearing of a motor. Background technique [0002] Rolling bearings are important supporting parts of motors and are also vulnerable parts. They are widely used in various large-scale industrial equipment, such as wind turbines, aero engines, high-speed rail EMUs, etc. Rolling bearing faults can cause equipment downtime to affect operation and production, and serious accidents can cause catastrophic accidents. Local pitting failure is a common failure form of rolling bearings. Therefore, monitoring and diagnosing local pitting faults of motor rolling bearings is an effective way to reduce operation and maintenance losses and ensure safe operation of equipment, which has important engineering significance. Vibration monitoring is one of the most effective ways to diagnos...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the problems existing in the prior art, the present invention provides a local pitting fault diagnosis method and its diagnosis system for motor rolling bearings. The proposed bearing vibration signal processing flow is used to solve the problem of cyclostationary feature identification under strong noise, which is beneficial to strong Cyclostationary feature identification under noise

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