Bearing fault diagnosis method based on quantum genetic algorithm optimized support vector machine

A technology of quantum genetic algorithm and support vector machine is applied in the field of bearing fault diagnosis based on quantum genetic algorithm optimization of support vector machine, which can solve the problems of long algorithm training time and the need to improve the accuracy of diagnosis.

Inactive Publication Date: 2016-10-26
GUANGDONG UNIV OF PETROCHEMICAL TECH
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  • Description
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AI Technical Summary

Problems solved by technology

The existing bearing fault diagnosis methods have the problems of long training time of the algorithm and the accuracy of the diagnosis needs to be improved

Method used

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  • Bearing fault diagnosis method based on quantum genetic algorithm optimized support vector machine
  • Bearing fault diagnosis method based on quantum genetic algorithm optimized support vector machine
  • Bearing fault diagnosis method based on quantum genetic algorithm optimized support vector machine

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0051] see figure 1 , the bearing fault diagnosis method based on the quantum genetic algorithm optimization support vector machine of the present embodiment includes the following steps:

[0052] Step 1. Collect vibration signals

[0053] Use the vibration measurement system to collect the vibration signal of the bearing;

[0054] Step 2. Calculate the dimensionless index

[0055] Use the collected vibration signals to calculate five dimensionless indicators: waveform indicators, pulse indicators, margin indicators, peak indicators and kurtosis indicators, and obtain training sample data sets and test sample data sets;

[0056] Waveform indicators:

[0057] Pulse indicator:

[0058] Margin indicator:

[0059]Peak metrics:

[0060] Kurtosis index:

[0061] In the formula, Indicates the average amplitude, X rms stands for root mean square, X max Indicates the largest square root, X r Indicates the square root amplitude, x indicates the vibration amplitude,...

Embodiment 2

[0096] The main technical solution of this embodiment is basically the same as that of Embodiment 1, and the features not explained in this embodiment are explained in Embodiment 1, and will not be repeated here. In this example,

[0097] The dimensionless index obtained through the calculation of the collected vibration signal through the second step is used as the test sample set of the fifth step.

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Abstract

A bearing fault diagnosis method based on a quantum genetic algorithm optimized support vector machine of the invention comprises the following steps: (1) acquiring a vibration signal of a bearing; (2) calculating dimensionless indexes; (3) optimizing model parameters C and Sigma of a support vector machine based on a cloud model quantum genetic algorithm; (4) training a support vector machine model; (5) performing fault diagnosis using the support vector machine model; and (6) outputting a bearing fault diagnosis result. The bearing fault diagnosis method of the invention has the advantage of high diagnosis accuracy. A new method is provided for solving the problem of bearing fault diagnosis.

Description

technical field [0001] The invention relates to a bearing fault diagnosis method, in particular to a bearing fault diagnosis method based on quantum genetic algorithm optimization support vector machine. Background technique [0002] With the advancement of science and technology, the internal structure of mechanical equipment is becoming more and more complex, the functions are becoming more and more powerful, and the level of intelligence is getting higher and higher. Bearings are the core components of machinery. If the bearings of mechanical equipment fail during operation, the entire production process may be interrupted, which not only affects the economic benefits of the enterprise, but also may endanger the personal safety of the staff, and even bring catastrophic consequences. In order to avoid losses caused by the failure of mechanical bearings, it is necessary to monitor the operating conditions of mechanical bearings, to predict failures before failures or to qu...

Claims

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Application Information

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IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 朱兴统熊建斌许波
Owner GUANGDONG UNIV OF PETROCHEMICAL TECH
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