Abstract
In view of failure characteristics of wind turbine gear box, this paper put forward a method for fault diagnosis based on the ensemble local means decomposition (ELMD) and fuzzy C-means clustering (FCM) method. By resolving the vibration signal of different fault state of high speed gear box by ELMD, the PF component was obtained with its singular value, which was composed of known sample followed by a test sample as the feature vector. The known sample was clustered by using the FCM clustering, and the test sample was recognized and classified . The experimental results show that the method for fault diagnosis based on ELMD and FCM clustering has good diagnosis results.
Highlights
With the gradually decreasing cost of wind turbine manufacturing, wind power has become the preferred alternative energy [1, 2]
The method of extracting fault features is the key to improve the diagnosis accuracy [4, 5]
By calculating singular value of the PF component after EMD decomposition of fault vibration signal, the feature vector can be obtained, which is applied in clustering recognition of fault signal as the input of fuzzy C-means clustering (FCM) clustering
Summary
With the gradually decreasing cost of wind turbine manufacturing, wind power has become the preferred alternative energy [1, 2]. Fault diagnosis of wind power generator includes two aspects, which are; feature extraction and fault diagnosis. The method of extracting fault features is the key to improve the diagnosis accuracy [4, 5]. The mechanical fault signal of wind turbine is a complicated time-varying and non-stationary signal and it is hard to process the fault signal by general frequency analysis methods. As an important part of wind turbine, the gear box is used to achieve the speed of the wind turbine rotor. The fault diagnosis method was used based on ELMD decomposition and FCM clustering in this paper. By calculating singular value of the PF component after EMD decomposition of fault vibration signal, the feature vector can be obtained, which is applied in clustering recognition of fault signal as the input of FCM clustering
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