Wind Turbine Bearing Fault Diagnosis Based on Sparse Representation of Condition Monitoring Signals

Abstract

An effective way to save the costs for data storage and transmission in bearing fault diagnosis of wind turbines is to use a sparse representation of massive condition monitoring signals. A proper dictionary that is adaptive to the analyzed signal containing time-varying components is a key issue to improve the sparse level in the sparse representation method, which has not been addressed in the literature. This paper explores a new sparse representation method that uses a new time-varying cosine-packet dictionary for the bearing fault diagnosis of wind turbines operating under varying speed conditions. First, the time-varying shaft rotating frequency (SRF) of a wind turbine is estimated from a generator current signal recorded synchronously with the vibration signal. Then, the new dictionary is designed, in which the basic functions, called time-varying cosine packet, change with the SRF. Finally, a sparse coefficient spectrum (SCS) of the vibration envelope signal is constructed by the sparse coefficients of the signal projection on the new dictionary. The merits of the proposed sparse representation method are that the dictionary designed is adaptive to the variations of major frequencies of the vibration signals, and the nonzero sparse coefficients over the dictionary designed have a clear physical meaning, i.e., representing the amplitude weights of the major order components contained in the vibration envelope signals. Therefore, the possible bearing fault characteristic orders can be identified from the SCS of the vibration envelope signal. Laboratory and field test results show that the sparse coefficients obtained by the new sparse representation method are suitable for the representations of the bearing vibration signals and the SCS is effective for bearing fault diagnosis of direct-drive wind turbines.