Abstract
Wind turbines work in strong background noise, and multiple faults often occur where features are mixed together and are easily misjudged. To extract composite fault of rolling bearings from wind turbines, a new hybrid approach was proposed based on multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) and the 1.5-dimensional Teager kurtosis spectrum. The composite fault signal was deconvoluted using the MOMEDA method. The deconvoluted signal was analyzed by applying the 1.5-dimensional Teager kurtosis spectrum. Finally, the frequency characteristics were extracted for the bearing fault. A bearing composite fault signal with strong background noise was utilized to prove the validity of the method. Two actual cases on bearing fault detection were analyzed with wind turbines. The results show that the method is suitable for the diagnosis of wind turbine compound faults and can be applied to research on the health behavior of wind turbines.
Highlights
As a renewable and clean energy source worldwide, wind energy has gradually received increasing attention
The specific implementation process is as follows: (1) First, multi-point optimal minimum entropy deconvolution adjusted (MOMEDA) preprocessing is performed by setting deconvolution periods of different faults; (2) 1.5-dimensional Teager kurtosis spectrum analysis is performed on the deconvolved signal preprocessed by MOMEDA; (3) According to bearing fault frequency and the results in previous step, the type of composite fault for the bearing is detected
(1) First, MOMEDA preprocessing is performed by setting deconvolution periods of different faults; (2) 1.5-dimensional Teager kurtosis spectrum analysis is performed on the deconvolved signal preprocessed by MOMEDA; (3)2020, According to bearing fault frequency and the results in previous step, the type of composite
Summary
As a renewable and clean energy source worldwide, wind energy has gradually received increasing attention. MOMEDA has improved the definition of deconvolution for the characteristics of rotating machinery fault signals, and introduces the target vector and multi-point D-norm to provide a non-iterative optimal solution. In this algorithm, continuous pulses are obtained by multi-point kurtosis deconvolution, which is made available for periodic fault feature extraction. A new approach was proposed here based on MOMEDA and the 1.5-dimensional Teager kurtosis spectrum to extract composite fault features for wind turbines. When the target vector t is completely matched with the original impact signal y , the deconvolution effect is optimal At this time, the multi-point D-norm obtains the maximum value, and the corresponding filter is a set of optimal filter f. The method can realize bearing fault diagnosis in wind turbines, demodulating fault characteristic frequency successfully and extracting the weak shock fault characteristics of the bearing
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