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DA-SVM-based rolling bearing fault detection method

A DA-SVM and rolling bearing technology, which is applied in the direction of instruments, character and pattern recognition, calculation models, etc., can solve the problems of low detection speed and detection accuracy, and achieve high fault detection accuracy, strong optimization ability, and detection speed fast effect

Pending Publication Date: 2019-10-01
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

The detection speed and detection accuracy of rolling bearings in the prior art are low and cannot meet the requirements of high standards

Method used

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  • DA-SVM-based rolling bearing fault detection method
  • DA-SVM-based rolling bearing fault detection method
  • DA-SVM-based rolling bearing fault detection method

Examples

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Embodiment 1

[0053] Example 1: Such as Figure 1-7 As shown, a DA-SVM-based rolling bearing fault detection method includes the following steps:

[0054] Step 1. The acceleration sensor synchronously acquires the signal generated by the rolling bearing rotation, and then decomposes and reconstructs the acquired original signal by wavelet packet, and extracts the energy eigenvalues ​​of the 8 nodes in the third layer, which is used as the eigenvector. As shown in Table 1, it lists some sample energy characteristic values ​​of four types of signals: normal state of rolling bearing, rolling element failure, outer ring failure, and inner ring failure. among them. The serial numbers 1-5 indicate the sample energy characteristic values ​​under normal conditions, the serial numbers 6-10 indicate the sample energy characteristic values ​​of shaft failure, the serial numbers 11-15 indicate the sample energy characteristic values ​​of the outer ring failure; the serial numbers 16-20 indicate the inner...

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Abstract

The invention relates to a DA-SVM (dragonfly algorithm optimized support vector machine)-based rolling bearing fault detection method, and belongs to the field of mechanical automation fault detection. The DA-SVM-based rolling bearing fault detection method comprises the following steps: firstly, performing feature extraction on signals of four states, namely a normal state, an outer ring fault, an inner ring fault and a shaft body fault, of the rolling bearing by using a wavelet packet method; and subjecting the extracted signals to multi-wavelet processing and then inputting the extracted signals into an SVM model optimized through a dragonfly algorithm to serve as training samples, and after training is completed, inputting the test samples into the trained DA-SVM model for fault detection. The detection recognition rate and the detection speed are important bases for measuring the detection method, and the DA-SVM rolling bearing diagnosis model formed by optimizing penalty factorsand kernel function parameters in the SVM through the dragonfly algorithm has the excellent performance of high diagnosis precision and high detection speed.

Description

Technical field [0001] The invention relates to a DA-SVM-based rolling bearing fault detection method, which belongs to the field of mechanical automation fault detection. Background technique [0002] As a key component of a large-scale machine, rolling bearings play an extremely important role in the operation of the entire mechanical equipment. If they fail, the loss will be huge. Rolling bearings are usually in a complex and harsh working environment for a long time, and the operating status is not easy to monitor. Therefore, timely understanding of the health of rolling bearings can timely monitor and feedback on various faults of rolling bearings, which is important for ensuring the safe operation of the entire large-scale machinery and equipment. Very important meaning. The detection speed and detection accuracy of rolling bearings in the prior art are both low and cannot meet the requirements of high standards. Summary of the invention [0003] The problem to be solved b...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/00
CPCG06N3/006G06F18/22G06F18/2411G06F18/214
Inventor 王海瑞李众亓晓蒙燕志星朱建府岳跃华李卓漫李明骏罗源睿刘国辉王广雪刘毅凡
Owner KUNMING UNIV OF SCI & TECH
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