A Rolling Bearing Degradation Index Extraction Method Based on Time-Frequency Feature Separation

Abstract

A rolling bearing degradation index extraction method based on time-frequency feature separation. Firstly, the time-frequency spectrum of the bearing original signal is calculated; secondly, by determining the number of maximum points at the same time and maximum points at the same frequency in the time-frequency matrix, Determine the position and range of harmonic distribution based on the row accumulation results of the time-frequency matrix, retain the harmonic distribution area, filter out other areas, and inverse short-time Fourier transform the processed time-frequency matrix to the time domain to realize the extraction of harmonic components ; After subtracting the harmonic components from the original signal, the threshold value noise reduction is carried out to extract the fault impact components, and finally the relative root mean square value is calculated for the extracted fault impact components as the rolling bearing degradation index; the present invention pre-filters based on time-frequency characteristics The prominent harmonic components in the time spectrum are removed to enhance the fault impact components, avoiding serious distortion when threshold noise reduction is used to extract the impact components, and the rolling bearing degradation index is calculated for the extracted fault impact components, which can effectively characterize the bearing degradation process.

Description

Technical field [0001] The present invention belongs to the field of rolling bearing fault diagnosis, specifically relates to a rolling bearing degradation index extraction method based on time-frequency feature separation. Background [0002] As a key component of rotating equipment, the health status of rolling bearings plays a decisive role in the safe operation of the entire equipment, so it is necessary to achieve monitoring of the operating state and degradation process of rolling bearings. [0003] Rolling bearing failures are often manifested as local damage such as pitting, cracking and spalling of the bearing working surface, and during the work, these local damages are struck, which will produce periodic impact components. However, due to the complexity of the industrial drivetrain and the time-varying nature of the operating conditions, the original signal contains a large number of interference signals, so how to accurately extract the impact component from the compl...

AI Technical Summary

Problems solved by technology

However, due to the complexity of industrial transmission systems and the time-varying nature of operating conditions, the original signal contains a large number of interference signals. Therefore, how to accurately extract the impact component from the complex component has become the research focus of describing the degradation process of rolling bearings.

The original signal is affected by components such as electromagnetic frequency and frequency conversion related frequency. The signal contains a large number of prominent harmonic components, and the impact components are submerged, resulting in serious distortion of the impact components extracted by the existing technology, and the extracted degradation indicators cannot represent The real degradation process of bearings

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