Wind turbine bearing fault diagnosis method based on multi-channel deep convolutional neural network
A deep convolution and neural network technology, applied in the field of wind turbine bearing fault diagnosis, can solve problems such as low signal-to-noise ratio, difficulty in application, and degradation of fault sample diagnosis performance, and achieve feature engineering avoidance, good versatility, and scalability , to achieve the effect of automatic feature learning
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[0043]The specific implementation will be described in detail below in conjunction with the accompanying drawings.
[0044] A deep convolutional neural network-based fault diagnosis method for wind turbine bearings. The method includes diagnostic signal collection, diagnostic model establishment, model training and evaluation, and model application.
[0045] A wind turbine bearing fault diagnosis method based on deep convolutional neural network, such as figure 1 shown, including the following steps:
[0046] Step 1: Use the vibration acceleration sensor to simultaneously collect the high-frequency vibration acceleration signals of the driving end and the non-driving end of the test bearing in various states.
[0047] Step 2: Apply time-frequency analysis technology to the collected vibration signal to obtain the corresponding time-frequency spectrum.
[0048] Step 3: Establish a deep convolutional neural network diagnostic model, and use the time-frequency spectrum and bear...
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