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

A rolling bearing, time-frequency characteristic technology, applied in the field of rolling bearing fault diagnosis, can solve problems such as inability to characterize bearing degradation process, impact component distortion, etc.

Active Publication Date: 2022-06-07
XI AN JIAOTONG UNIV +1
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  • Abstract
  • Description
  • Claims
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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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  • A Rolling Bearing Degradation Index Extraction Method Based on Time-Frequency Feature Separation
  • A Rolling Bearing Degradation Index Extraction Method Based on Time-Frequency Feature Separation
  • A Rolling Bearing Degradation Index Extraction Method Based on Time-Frequency Feature Separation

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Embodiment Construction

[0062] The present invention is further described in detail below in conjunction with embodiments and accompanying drawings.

[0063] as Figure 1 As shown, a method for obtaining the degradation trend of rolling bearings based on time-frequency characteristic analysis, using the original signal of bearing outer ring failure in XJTU-SY bearing data, including the following steps:

[0064] Step 1: First of all, the original signal of the rolling bearing from normal to fault is collected at intervals by vibration acceleration sensor, and the data is collected every minute, each sampling time is 1.28s, the sampling frequency is 25.6KHz, and a total of 123 groups are collected; among them, the 10th group of original signals (before the fault) and the 80th group of original signals (after the failure) are the first 0.1s time domain waveform and the short-term Fourier transform time domain waveform Figure 2 、 Figure 3 As shown, in the time spectrum, the harmonic component behaves as prom...

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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...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/045G06F17/14G06F17/16
CPCG01M13/045G06F17/14G06F17/16
Inventor 王琇峰唐国运区瑞坚郭美娜
Owner XI AN JIAOTONG UNIV
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