Wind turbine generator gear case fault diagnosis method based on VMD and FA_PNN

A technology for wind turbines and fault diagnosis, which is applied in the testing of mechanical components, testing of machine/structural components, and measuring devices, and can solve problems such as the impact of classification effects

Inactive Publication Date: 2019-12-20
XIAN UNIV OF TECH
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Problems solved by technology

Compared with BP neural network, probabilistic neural network (PNN) has fast training efficiency and convergence speed, but its classification effect is easily affected by the selection of smoothing factor σ

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  • Wind turbine generator gear case fault diagnosis method based on VMD and FA_PNN
  • Wind turbine generator gear case fault diagnosis method based on VMD and FA_PNN
  • Wind turbine generator gear case fault diagnosis method based on VMD and FA_PNN

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Embodiment Construction

[0100] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0101] Variational mode decomposition is a new adaptive and non-recursive signal processing method. The algorithm is divided into two processes: construction and solution of variational problems. The optimal solution of the sub-problem is solved, and the optimal solution of the variational problem is obtained through continuous iteration to determine the center frequency and bandwidth of each IMF, and realize the adaptive decomposition of the signal frequency band and the separation of the natural mode components.

[0102] The present invention is based on VMD and FA_PNN wind turbine gearbox fault diagnosis method. First, detrend preprocessing is performed on the gearbox vibration signal collected by the sensor, and then VMD is performed on the processed gearbox vibration signal under different decomposition numbers and penalty factors. Varia...

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Abstract

The invention discloses a wind turbine generator gear case fault diagnosis method based on VMD and FA_PNN. Firstly, gear case vibration signals acquired by a sensor are subjected to de-trending processing, then, the processed gear case vibration signals are subjected to VMD variation modal decomposition under the condition of different decomposition numbers and penalty factors, k modal componentsare obtained with a Pearson's correlation coefficient method, singular value entropy, power spectral entropy, marginal spectral entropy and instantaneous energy spectral entropy of the k modal components are extracted from three angles of time domain, frequency domain and time-frequency domain, a feature vector matrix capable of describing operating states of a wind turbine generator gear case ina quantization manner is formed, and finally, test sample data are tested with well-trained firefly optimized probabilistic neural network FA_PNN, so that fault diagnosis of the wind turbine generatorgear case is completed. Classified recognition of faults of the wind turbine generator gear case is realized.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of a gearbox of a wind turbine, and in particular relates to a fault diagnosis method for a gearbox of a wind turbine based on VMD and FA_PNN. Background technique [0002] With the extensive use of wind turbines, fault diagnosis of wind turbines has become a new problem that needs to be solved urgently. The most critical gear box in the wind turbine transmission system, due to the harsh working environment, often causes cracks, peeling and other failures, and its failures account for about 60% of the total failures of the unit. Therefore, timely and accurate diagnosis of wind turbine gearbox faults is of great significance to improve the efficiency and reliability of wind turbines and reduce operation and maintenance costs. [0003] The randomness of wind energy makes the wind turbine gearbox operate in non-stationary conditions all the year round, and its vibration signals often have th...

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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 田录林贾嵘罗燚王伟博陈倩雯张沛文张盛炜巨思远
Owner XIAN UNIV OF TECH
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