Feature extraction of rotor fault based on EEMD and curve code

Feature extraction is an important part of fault diagnosis. Rotor faults are of various types with little differences between each other and rely on effective methods of feature extraction to be recognized correctly. There are many methods applied to feature extraction at present. However, fault features computed are generally based on fixed samples, which may lead to an unsatisfactory result for other samples. To solve this problem, a novel feature extraction method based on Ensemble Empirical Mode Decomposition (EEMD) and Curve Code (CC) is presented in this paper. The noisy vibration signals are first denoised using wavelet thresholding-based de-noising technology. Then EEMD is applied to decompose the de-noised signals to obtain all Intrinsic Mode Functions (IMFs), and the corresponding Standard Deviations (SDs) are calculated. Finally, the curve plotted by the SDs is coded into a digital sequence called CC as a fault feature. Results of comparative experiments, designed using a large number of samples from rotor laboratory bench, prove the validity of the proposed method and show that the proposed method has higher and more stable Fault Discrimination Rate (FDR) for rotor fault recognition. (C) 2018 Published by Elsevier Ltd.