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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Problems solved by technology

[0003] While the scale of wind power generation continues to expand, the problem of operation and maintenance of wind turbines is also becoming more and more prominent, especially due to the high cost of operation and maintenance due to failure and maintenance, and the maintenance cost even takes up 10-25% of the total investment. Restricted by the geographic location of wind energy resources, existing large-scale wind farms are generally built in remote areas such as northwest my country, plateaus, and coastal areas. The operating environment of wind turbines is relatively harsh, and transportation conditions are inconvenient, which brings Wind turbines may be affected by extreme weather such as heavy rainfall, snowfall, storms and lightning. With the increase of operating time, various abnormalities and damages to wind turbine blades, gearboxes, generators and other components are prone to occur. Faults lead to abnormal operation or even shutdown of the unit. Among them, the gearbox, as an important transmission component in the wind turbine, shows a high failure rate in actual operation. According to statistics, about 20% of the downtime is caused by the gearbox failure. Most of the failures occur on the bearings, so the bearing failure is the main factor causing the failure of the fan gearbox

[0004] Health status monitoring is indispensable to ensure the safe and stable operation of wind turbines. Effective fault diagnosis technology can detect the faults of wind turbines in an early stage. Due to the continuous expansion of wind power installed capacity, traditional regular maintenance methods have become increasingly It cannot meet the actual operation and maintenance needs of wind farms, and then there is a condition monitoring technology, which uses sensors to obtain real-time operating data of wind turbines to achieve the purpose of online monitoring and fault diagnosis, but this depends on expert knowledge and work experience, and costs a lot of money A large amount of manpower, material resources and time costs, coupled with the increase in system complexity, increase in fault diversity, increase in data volume, and the need for online diagnosis, it has become increasingly difficult to rely on traditional methods for effective fault diagnosis. To solve these problems with effective intelligent diagnosis technology, we propose a method for audio recognition and fault diagnosis of wind turbine mechanical faults

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