Wind turbine generator gear case combined fault diagnosis method based on support vector machine probability estimate

A technology of support vector machine and probability estimation, applied in the testing of machine gear/transmission mechanism, testing of mechanical components, testing of machine/structural components, etc., to achieve the effect of improving flexibility and stability

Active Publication Date: 2017-08-11
ZHEJIANG UNIV
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[0005] In order to improve the existing method, the purpose of the present invention is to provide a ...

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  • Wind turbine generator gear case combined fault diagnosis method based on support vector machine probability estimate
  • Wind turbine generator gear case combined fault diagnosis method based on support vector machine probability estimate
  • Wind turbine generator gear case combined fault diagnosis method based on support vector machine probability estimate

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Embodiment Construction

[0033] The concrete implementation method of the present invention will be further described below in conjunction with accompanying drawing:

[0034] figure 1 It is a schematic diagram of the overall algorithm framework of the present invention. As shown in the figure, assuming that the sample signal is collected from M sensors on the gearbox, the sample x can be decomposed into x according to the sensor 1 ,x 2 ,...,x M , respectively extract the vibration feature f 1 , f 2 ,..., f M , and build different support vector machine models. When a new sample is input, the final conclusion is drawn by synthesizing the diagnosis results of all support vector machine models.

[0035]In order to verify the effectiveness of the present invention, several groups of simulation signals are constructed by Matlab to simulate the vibration signals of the gearbox. The setting of the simulation experiment is as follows: the number of sensors M=2, sensor 1 and sensor 2 can be used, and it...

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Abstract

The invention discloses a wind turbine generator gear case combined fault diagnosis method based on support vector machine probability estimate. The wind turbine generator gear case combined fault diagnosis method based on support vector machine probability estimate mainly includes two modules: the first module is vibration signal processing and a fault feature extraction algorithm, based on ensemble empirical mode decomposition (EEMD); and the second module is a training model method and a probability estimate algorithm of the support vector machine. The wind turbine generator gear case combined fault diagnosis method based on support vector machine probability estimate respectively establishes models for different sensors, integrates the results of all the classifiers during the analysis process, and improves the accuracy of diagnosis. The wind turbine generator gear case combined fault diagnosis method based on support vector machine probability estimate takes a simulation signal as a test object, provides a detailed algorithm description, and verifies the validity of the algorithm at the gear case combined fault diagnosis aspect through the experiment.

Description

technical field [0001] The invention belongs to the field of fault feature extraction and fault diagnosis methods, in particular to a fault feature extraction and fault diagnosis method for a gearbox of a wind power generating set based on support vector machine probability estimation. Background technique [0002] In the past few years, with the rapid development of the wind power industry, a large number of wind turbines have been deployed and built, and the capacity of wind power units has also been increasing. Accidents caused by wind turbine failures have occurred from time to time, causing huge economic The loss has hindered the development speed of the wind power industry. The gearbox is located in the wind turbine nacelle. It is the main component of the wind turbine transmission power and an important hub connecting the main shaft and the generator. Its operation will be disturbed by many factors, such as wind speed fluctuations and dynamic changes in load. At the ...

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Application Information

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IPC IPC(8): G01M13/02
CPCG01M13/028
Inventor 杨强胡纯直颜文俊杨茜黄淼英
Owner ZHEJIANG UNIV
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