Wind driven generator variable pitch bearing fault diagnosis method and device based on neural network

Abstract

The invention discloses a fault diagnosis method and device for a variable pitch bearing of a wind driven generator based on a neural network, and the method comprises the steps: measuring different azimuth angles of a blade and the signal intensity of different point locations of a sensor, determining the optimal measurement azimuth angle of the blade and a point location arrangement scheme of the sensor, fixing the blade at the optimal azimuth angle, collecting variable pitch vibration data, and carrying out the fault diagnosis. Further processing the collected vibration data into a data set, constructing a neural network model, training a network by using the collected data set, and deploying the trained network into a PLC (Programmable Logic Controller) to dynamically monitor the fan in real time; the device comprises a vibration sensor, a data acquisition card and a programmable logic controller (PLC). According to the invention, the neural network algorithm is applied to the fault diagnosis of the variable-pitch bearing of the wind driven generator, the network is trained by using the historical vibration data, and then the fault diagnosis is carried out by using the trained network, so that the health condition of the variable-pitch bearing can be rapidly and accurately monitored in real time.

Description

technical field [0001] The invention relates to the technical field of wind power generation and the technical field of bearing fault diagnosis, in particular to a neural network-based fault diagnosis method and device for pitch bearings of wind power generators. Background technique [0002] During the operation of the wind turbine, the pitch bearing is an important supporting component for the blade adjustment angle of the wind turbine. The wind turbine pitch bearing is an important part connecting the wind turbine hub and blades, and is responsible for transmitting loads. When the wind turbine pitch bearing is in working condition, when the various components inside the pitch bearing are squeezed or the components are worn out, the working state of the pitch bearing is constantly changing, and this changing working state will largely affect the Resulting in failure of pitch bearings. Wind turbine pitch bearings have high precision, high failure rate, and large economic ...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the shortcomings of the existing technology, in the fault diagnosis process, overcome the shortcomings of high requirements for prior knowledge, low analysis efficiency of a large amount of time-domain data, and low signal-to-noise ratio in the process of pitch change, and realize the goal of improving the accuracy of fault diagnosis and analysis. Purpose, the present invention adopts following technical scheme:

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