Wind turbine generator set mechanical fault audio identification and fault diagnosis method

Abstract

The invention belongs to the technical field of wind power generator fault diagnosis, and particularly relates to a wind power generator set mechanical fault audio recognition and fault diagnosis method which comprises fault signal detection and a convolutional neural network. The fault signal detection comprises vibration signal detection, acoustic emission signal detection, strain force signal detection, temperature signal detection, oil parameter detection and electric signal detection. A set of method flow based on vibration fault signal monitoring and a convolutional neural network model is provided for intelligent bearing fault diagnosis, a vibration signal is obtained by an acceleration sensor, historical data is subjected to reasonable sampling and 1D-2D signal processing conversion, and then the vibration signal is obtained by the acceleration sensor. Bearing signal samples in various fault states are reasonably divided into a training set and a test set, the training set is sent to the established deep convolutional neural network for model learning, and after model learning is completed, the test set is used for verifying the generalization ability of the model, namely the test accuracy.

Description

technical field [0001] The invention relates to the technical field of fault diagnosis of wind power generators, in particular to a method for audio recognition and fault diagnosis of mechanical faults of wind power generators. Background technique [0002] With the continuous progress of science and technology and the continuous growth of economic development, people's living standards have been improved unprecedentedly. Coupled with the rapid changes in industrial manufacturing and product production, energy supply is facing unprecedented challenges. In the context of energy shortages, clean and renewable energy More and more attention has been paid to the development of energy. Among them, wind energy, as a safe and clean renewable energy, has great development potential and is an important alternative energy for traditional fossil fuels. With the continuous development and maturity of wind power technology, wind power generation Entering a period of rapid development, wind...

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