Rolling bearing degradation index extraction method based on time-frequency feature separation

Abstract

The invention discloses a rolling bearing degradation index extraction method based on time-frequency feature separation. The method comprises the steps of firstly calculating a time-frequency spectrum of a bearing original signal, secondly, determining the number of maximum value points at the same moment and the number of maximum value points at the same frequency in the time-frequency matrix, determining the harmonic distribution position and range in combination with the line accumulation result of the time-frequency matrix, retaining a harmonic distribution area, filtering other areas, and performing inverse short-time Fourier transform on the processed time-frequency matrix to a time domain so as to realize extraction of harmonic components, after subtracting the harmonic component from the original signal, extracting the fault impact component through threshold noise reduction, and finally calculating the relative root-mean-square value of the extracted fault impact component to serve as a rolling bearing degradation index. According to the method, the prominent harmonic components in the time-frequency spectrum are filtered in advance based on the time-frequency characteristics so as to enhance the fault impact components, serious distortion generated when the impact components are extracted through threshold noise reduction is avoided, the rolling bearing degradation index is calculated for the extracted fault impact components, and the index can effectively represent the bearing degradation process.

Description

technical field [0001] The invention belongs to the field of rolling bearing fault diagnosis, and in particular relates to a rolling bearing degradation index extraction method based on time-frequency feature separation. Background technique [0002] Rolling bearings are key components of rotating equipment, and their health status plays a decisive role in the safe operation of the entire equipment. Therefore, it is very necessary to monitor the operating status and degradation process of rolling bearings. [0003] Rolling bearing faults often manifest as local damage such as pitting, cracks and peeling on the working surface of the bearing. During the working process, these local damages are impacted, which will produce periodic impact components. However, due to the complexity of the industrial transmission system and the time-varying operating conditions, the original signal contains a large number of interference signals. Therefore, how to accurately extract the impact c...

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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