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Wind generating set gearbox fault positioning method based on CMS system big data in combination with standard deviation and wavelet entropy

A technology for wind turbine and fault location, which is applied in the testing of machines/structural parts, testing of mechanical parts, measuring devices, etc. It can solve the problems of slow calculation and large amount of data, and achieve the effect of good positioning effectiveness

Active Publication Date: 2020-09-29
ZHEJIANG UNIV OF TECH +1
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Problems solved by technology

However, the amount of data required by these methods is huge, and the calculation is slow, and the analysis is often based on historical data, so it is impossible to determine whether a fault has occurred in the first place

Method used

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  • Wind generating set gearbox fault positioning method based on CMS system big data in combination with standard deviation and wavelet entropy
  • Wind generating set gearbox fault positioning method based on CMS system big data in combination with standard deviation and wavelet entropy
  • Wind generating set gearbox fault positioning method based on CMS system big data in combination with standard deviation and wavelet entropy

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Embodiment Construction

[0033] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be further described below in conjunction with the accompanying drawings and actual experiments.

[0034] refer to Figure 1 to Figure 5 , a wind turbine gearbox fault location method based on CMS system big data combined with standard deviation and wavelet entropy, first calculate the standard deviation of the original vibration data and perform filtering, establish a Gaussian model of the standard deviation of the normal interval, and use this model to judge Whether there is a fault in the system, on the basis of the fault, calculate the wavelet entropy of the vibration data to judge the fault type of the gearbox.

[0035] The wind turbine gearbox fault location method based on CMS system big data combined with standard deviation and wavelet entropy of the present invention comprises the following steps:

[0036] 1) Ca...

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Abstract

The invention discloses a wind generating set gearbox fault positioning method based on CMS system big data in combination with standard deviation and wavelet entropy. The method comprises the steps of 1) calculating the standard deviation of original vibration data and filtering; 2) establishing Gaussian model of the standard deviation of a normal interval, and judging whether fault occurs in thesystem or not by using the model, and 3) calculating wavelet entropy of the vibration data to judge the fault type. The method has the advantages that the positioning effectiveness is good, and the requirements of practical application are met.

Description

technical field [0001] The invention belongs to the field of big data analysis of industrial control systems, and specifically provides a wind turbine gear box fault location method based on CMS system big data combined with standard deviation and wavelet entropy. It can accurately warn whether the gear box is faulty according to the CMS vibration data. Moreover, high-speed shaft faults and non-high-speed shaft faults are distinguished to provide a safety situation assessment for the fan system and ensure the safe operation of the fan. Background technique [0002] When the wind turbine is in operation, faults such as gear jamming, blade cracks, and high-speed shaft pitting may occur, resulting in serious economic losses. At the same time, simple fault detection can no longer meet the requirements of enterprises and society, and the demand for fault location is increasing. Therefore, the fault early warning and location of wind turbines plays a very important role in the op...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/021G01M13/028
CPCG01M13/021G01M13/028
Inventor 张文安黄大建顾曹源徐博文郭方洪朱俊威刘伟江史晓鸣
Owner ZHEJIANG UNIV OF TECH
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