A Fault Feature Extraction Method of Rolling Bearing Acoustic Signal Based on STFT and Moment of Inertia Entropy

Abstract

The invention discloses a rolling bearing sound signal fault feature extraction method based on an STFT and a rotation inertia entropy. A common rolling bearing fault feature extraction method is based on a rolling bearing vibration signal, however the requirement of a sensor by the collection of rolling bearing vibration data is very high, the equipment cost is increased, an intelligent mobile phone is taken as an important part of daily life, and the recording function of the mobile phone can collect a rolling bearing sound signal. The invention provides the rolling bearing sound signal fault feature extraction method based on short time Fourier transform (STFT) and a rotation inertia entropy, firstly the intelligent mobile phone is used to collect a rolling bearing fault sound signal, then the sound signal is subjected to Fourier analysis, a spectrogram matrix is obtained, then the modulus of the matrix is obtained, and the rotation inertia entropy of the spectrogram of the rotation inertia entropy is calculated. A test result analysis shows that the fault feature obtained by the method has an excellent classification characteristic and can support the fault diagnosis of a rolling bearing.

Description

technical field [0001] The invention relates to the technical field of rolling bearing testing, in particular to a rolling bearing sound signal fault feature extraction method based on STFT and moment of inertia entropy. Background technique [0002] Rolling bearings are one of the standard parts widely used in various mechanical equipment, and rolling bearing failures are also one of the most important causes of machine failures. According to statistics, about 30% of rotating machinery faults are related to rolling bearing faults, so it is very necessary to perform fault diagnosis on rolling bearings. [0003] One of the key technologies of fault diagnosis is feature extraction. A good fault feature extraction method is very important to improve the accuracy of fault diagnosis. Traditional rolling bearing fault diagnosis is generally based on the feature extraction of the vibration signal of the rolling bearing, and experienced maintenance personnel in many engineering fie...

AI Technical Summary

Problems solved by technology

[0007] The technical problem to be solved by the present invention is: the equipment cost is high in traditional vibration signal-based rolling bearing fault diagnosis, and the present invention uses smart phone recording, which greatly reduces the sampling cost; in addition, the present invention defines a new entropy—moment of inertia entropy, Represent the complexity of the time-frequency distribution, so that the obtained fault features have excellent classification characteristics

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