Fault diagnosis method of motor bearing based on time-frequency domain statistical characteristic

Abstract

The invention discloses a fault diagnosis method of a motor bearing based on a time-frequency domain statistical characteristic. The fault diagnosis method comprises the following steps that: speed pulse signals and vibration signals of the motor bearing in T time interval are collected simultaneously, and a frequency conversion curve f is calculated according to the speed pulse signals; the vibration signals at a stationary speed are intercepted according to the frequency conversion curve and are sectioned; health indicators of the vibration signals in all segments are calculated according toa formula of the health indicators, a fault degree grade of each segment is obtained by comparing the health indicators with a fault threshold table, and the fault grade with the highest fault degreegrades counted is the fault grade of the motor bearing; Hilbert envelope spectrums of the vibration signals in all segments are calculated, effective frequencies of all envelope spectrums are determined according to a calculation method of the effective frequencies, the most frequent occurrence frequency of the effective frequencies counted is a fault frequency, and then a fault position of the bearing is determined. The overall fault degree and the fault position of the motor bearing are accurately obtained by counting the fault grade of the vibration signals and the effective frequencies ofthe envelope spectrums in a plurality of time intervals.

Description

technical field [0001] The invention belongs to the technical field of signal processing analysis and fault diagnosis, in particular to a motor bearing fault diagnosis method based on time-frequency domain statistical characteristics. Background technique [0002] The working state of the motor bearing directly affects the operation safety and performance of the entire equipment. At present, there are many types of testing equipment and testing methods for the motor bearing, usually based on the relevant standards of the bearing industry, under a certain speed and test load, by measuring the bearing. Information such as vibration acceleration, temperature, and acoustic emission can be used to monitor, vibrate, and alarm the motor based on time-frequency domain characteristics. [0003] However, the existing fault diagnosis methods for motor bearings still have the problem of low fault diagnosis accuracy. The impact signals caused by bearing faults are usually mixed in the ba...

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Problems solved by technology

[0005] Aiming at the problems existing in the prior art, the present invention proposes a motor bearing fault diagnosis method based on time-frequency domain statistical characteristics, which can well solve the problems of poor signal extraction effect and low fault diagnosis accuracy in traditional methods. Analyze the time domain index and the energy distribution of the envelope spectrum diagram to determine the bearing fault degree and fault type

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