Abstract
Based on Multi-Masking Empirical Mode Decomposition (MMEMD) and fuzzy c-means (FCM) clustering, a new method of wind turbine bearing fault diagnosis FCM-MMEMD is proposed, which can determine the fault accurately and timely. First, FCM clustering is employed to classify the data into different clusters, which helps to estimate whether there is a fault and how many fault types there are. If fault signals exist, the fault vibration signals are then demodulated and decomposed into different frequency bands by MMEMD in order to be analyzed further. In order to overcome the mode mixing defect of empirical mode decomposition (EMD), a novel method called MMEMD is proposed. It is an improvement to masking empirical mode decomposition (MEMD). By adding multi-masking signals to the signals to be decomposed in different levels, it can restrain low-frequency components from mixing in high-frequency components effectively in the sifting process and then suppress the mode mixing. It has the advantages of easy implementation and strong ability of suppressing modal mixing. The fault type is determined by Hilbert envelope finally. The results of simulation signal decomposition showed the high performance of MMEMD. Experiments of bearing fault diagnosis in wind turbine bearing fault diagnosis proved the validity and high accuracy of the new method.
Highlights
Wind energy is one of the fast growing renewable energy resources, and is going to have remarkable share in the energy market [1]
Analysis only relying on time domain or frequency domain cannot meet the needs of the current mechanical fault diagnosis, time-frequency analysis has become a hot research topic [9]
This paper proposes an improved method to masking empirical mode decomposition (MEMD), named Multi-Masking Empirical Mode Decomposition (MMEMD), which can restrain low-frequency component from mixing in high-frequency component effectively in the sifting process, and suppress the mode mixing by adding multi-masking signals to the signals to be decomposed in different levels
Summary
Wind energy is one of the fast growing renewable energy resources, and is going to have remarkable share in the energy market [1]. The commonly used time-domain indicators include maximum, minimum, mean, mean square root and kurtosis value [7] Frequency domain analysis such as spectrum and envelope analysis is the most involved method in the mechanical fault diagnosis [8]. This paper proposes an improved method to MEMD, named MMEMD, which can restrain low-frequency component from mixing in high-frequency component effectively in the sifting process, and suppress the mode mixing by adding multi-masking signals to the signals to be decomposed in different levels. MMEMD changes the accuracy of extreme sampling by adding masking signals of different frequency to the signals to be decomposed in different decomposition levels, which can prevent lower frequency components effectively from being included in high frequency components in the process of sifting, and achieve the suppression of mode mixing as a result. Uik satisfies the following requirements [29]: N
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