Research On Vibration And Acoustic Diagnosis For Wind Turbine Main Bearing Based On Blind Source Separation

In recent years, along with the rapid increasing demand for energy and seriousenvironmental problem, the problem of coal, oil, natural gas and other traditional energybecome more and more serious. As a new renewable energy, wind energy has been paid moreand more attention for its advantages of huge reserves, low price and less environmentpollution. So the wind power equipment also obtained a rapid development. In recent years,with the vigorous development of wind power industry in China, the number of wind turbinesincrease gradually. Because of the frequent wind turbine accidents, it is particularly importantfor the state monitoring and fault diagnosis of wind power generator. Among all thecomponents of wind turbine, main bearing is the most important, also one of the most prone tofailure part. The working station of main bearing will directly affect the operation of the wholewind turbine. Therefore, condition monitoring and fault diagnosis of main bearing in windturbine is very necessary.At present there are quite a lot of diagnosis methods for main bearing in wind turbine. Themost commonly used methods are vibration diagnosis method and acoustic emission diagnosismethod. Due to the operating environment of wind turbine is often very poor, in the operationprocess, characteristic information which reflects the fault state is often submerged in noisejamming signals. Extracting the fault information effectively is of great importance inmonitoring and diagnosis main bearing of wind turbine. Many domestic and foreign scholarshave made great efforts in this respect. They acquire methods such as expert system, fuzzysystem, neural network, wavelet transform, Hilbert-Huang transform, Wigner distribution,support vector machine in the diagnosis of main bearing in wind turbines, which has mademany valuable research results, but there still exist some problems. In view of this, this paperexplores the extraction method of wind turbine main bearing vibration and acoustic emissionsignals based on blind source separation theory, and the following work has been done:Firstly, the development status of wind power technology is introduced and thebackground, purpose and significance of the research are expounded. The research situation ofvibration and acoustic emission diagnosis for main bearing in wind turbine at home andabroad is discussed, and the ideas and methods in this paper are pointed out. Secondly, the basic theory of blind source separation and the blind source separationalgorithm are discussed, mainly on FastICA algorithm and JADE algorithm, and theshortcomings of these algorithms are pointed out. In view of the existing problems of theseseparation algorithms, this paper discusses the optimization of particle swarm optimizationalgorithm for blind source separation process, and compared the performance of the variousseparation algorithm.Thirdly, vibration fault diagnosis system of wind turbine main bearing based on blindsource separation is established. The extraction method of vibration signals is discussed andenvelope analysis is regarded as an effective method in the extraction of vibration signals. Thevibration signals of rotor test-bed, the wind turbine test-bed and real wind turbine mainbearings are decomposed for the feature abstraction of vibration fault signal.Fourthly, acoustic emission fault diagnosis system of wind turbine main bearing based onblind source separation is established. The extraction method of acoustic emission signals isdiscussed and wavelet analysis is regarded as an effective method in the extraction of acousticemission signals. Then the wind turbine main bearing acoustic emission signals aredecomposed for the feature abstraction of acoustic emission fault signal.Fifthly, according to the characteristics signals of wind turbine main bearing, faultdiagnosis method of wind turbine main bearing based on integrated wavelet neural network isestablished. According to the characteristics of vibration signals and acoustic emission signals,sub neural network are designed and the decision fusion neural network is designed fordiagnosis information fusion, which has improved the efficiency of fault diagnosis. Softwareimplementation for the diagnosis algorithm has enhanced the practicability of diagnosticmethods.Sixthly, summarize the whole texts and prospect the relevant technique..