Construction method of wind driven generator bearing fault identification model based on deep learning

Abstract

The invention relates to a construction method of a motor bearing fault identification model, in particular to a construction method of a wind driven generator bearing fault identification model. Theinvention aims to provide a construction method of a wind driven generator bearing fault identification model based on deep learning by utilizing a deep learning method, and further realize fault identification and positioning of a wind driven generator bearing. The construction method can greatly improve the accuracy of fault prediction. Compared with other methods, for the construction method, the general applicability and generalization of the deep learning network model to wind driven generator bearing fault identification are greatly improved. According to the construction method, accurate identification of different faults of the existing wind driven generator bearings of various models is easy to realize. The construction method is realized by the following steps: 1, presetting bearing fault types and quantity; 2, acquiring and preprocessing an original signal; 3, creating and configuring a deep learning network; 4, training a network; and 5, verifying the network accuracy.

Description

technical field [0001] The invention relates to a method for constructing a motor bearing fault identification model, in particular to a method for constructing a wind power generator bearing fault identification model. Background technique [0002] Artificial intelligence technology is based on intelligent algorithms as the core, and studies the methods and technologies of using machines to imitate and expand human intelligence. With more and more data that can be collected, the in-depth research of artificial intelligence theory and the continuous improvement of hardware computing power, artificial intelligence technology is constantly penetrating into various application fields, mainly including natural language understanding, fault diagnosis and operation and maintenance management, intelligent robot Wait. Artificial intelligence has become the core driving force of a new round of technological revolution and industrial transformation, and it is having an extremely prof...

AI Technical Summary

Problems solved by technology

[0006] Disadvantages of the existing technical solution 1: the labor cost of the existing technical solution 1 is relatively high, and it is highly dependent on professional knowledge and domain expert experience

[0008] Disadvantages of the second prior art solution: usually only the mean value and variance of the vibration signal are considered, and the identification model is relatively simple. Fault identification with inconspicuous features such as outer ring peeling has the disadvantage of low accuracy

In addition, the generalization ability of the model fault identification is not strong under different working conditions and external noise interference. significantly reduce

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