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A Fault Diagnosis Method for Wind Power Gearbox

A wind power gearbox and fault diagnosis technology, which is applied in the testing of mechanical components, testing of machine/structural components, instruments, etc., can solve the problems of premature convergence in the solution process of QPSO algorithm, slow convergence speed of artificial neural network, etc., and achieve improvement Operational benefit, enhanced convergence precision and convergence speed, effect of improved accuracy

Inactive Publication Date: 2020-08-04
HONGHE COLLEGE
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AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a fault diagnosis method for a wind power gearbox, which solves the problems of over-fitting and slow convergence of the existing artificial neural network, and the premature convergence of the QPSO algorithm solution process

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  • A Fault Diagnosis Method for Wind Power Gearbox
  • A Fault Diagnosis Method for Wind Power Gearbox
  • A Fault Diagnosis Method for Wind Power Gearbox

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example

[0089] Taking a 1.5MW wind turbine in a wind farm as the research object, the gearbox is composed of one-stage planetary gear and two-stage parallel shaft gear transmission. The vibration acceleration signal on the surface of the parallel shaft gear collected by the acceleration sensor installed on the bearing seat of the high-speed shaft of the gearbox is decomposed to obtain the eigenvector, and an improved quantum particle swarm BP neural network (IQPSOBP) model is constructed for the wind power gearbox. Carry out troubleshooting. In addition, its diagnostic results are compared with the output results of BP neural network, particle swarm BP neural network (PSOBP) and quantum particle swarm BP neural network (QPSOBP).

[0090] According to the dimension of feature vector and fault type, the number of nodes in the input layer of BP neural network is set to 6, the number of nodes in the output layer is equal to 3, and the number of nodes in the hidden layer is set to 10. In ...

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Abstract

The invention discloses a fault diagnosis method for a wind power gear box which comprises the following steps of: firstly, extracting a vibration acceleration signal of the wind power gearbox, and establishing a fault set after decomposition; secondly, adopting a random adjustment scheme facing the contraction-expansion coefficient to enhance the robustness of the quantum-behaved particle swarm algorithm; thirdly, introducing a restart strategy into the quantum-behaved particle swarm algorithm in order to further increase the optimum probability that the algorithm jumps out of local; and finally, establishing a fault diagnosis model of the wind power gear box through a method combining the improved quantum-behaved particle swarm and a BP neural network. Compared with the schemes of the BPneural network, a particle swarm and a quantum-behaved particle swarm optimization BP network, the fault diagnosis method for the wind power gear box is higher in diagnosis precision, and reduces theoccurrence probability of severe accidents.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis of wind power generating sets, and in particular relates to a fault diagnosis method of a wind power gearbox. Background technique [0002] Gearboxes are one of the key components of wind turbines. Due to the influence of impact loads and alternating loads, gearbox wear, broken teeth and other faults occur frequently, and it has become a fault-prone area. According to statistics, gearbox failures account for about 60% of the total failures of wind turbines, and are an important factor inducing equipment failures. Therefore, establishing an effective gearbox fault identification model and grasping its health status in time can predict the impending fault of the gearbox in advance, avoid the probability of severe accidents, and help improve the operating efficiency of the wind farm. [0003] In the wind power gearbox fault diagnosis technology, the vibration signal analysis method is widel...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/021G06N3/02
CPCG01M13/021G06N3/02Y04S10/50
Inventor 程加堂段志梅何静松熊燕
Owner HONGHE COLLEGE
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