An improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis

Multi-fault identification is a challenge for rotating machinery fault diagnosis. The vibration signals measured from rotating machinery usually are complex, non-stationary and nonlinear. Especially, the useful multi-fault features are too weak to be identified at the early stage. In this paper, a novel method called improved EEMD with multiwavelet packet for rotating machinery multi-fault diagnosis is proposed. Using multiwavelet packet as the pre-filter to improve EEMD decomposition results, multiwavelet packet decomposes the vibration signal into a series of narrow frequency bands and enhances the weak multi-fault characteristic components in the different narrow frequency bands. By selecting the proper added noise amplitude according to the vibration characteristics, EEMD is further improved to increase the accuracy and effectiveness of its decomposition results. The proposed method is applied to analyze the multi-fault of a blade rotor experimental setup and an industrial machine set, and the results confirm the advantage of the proposed method over EEMD, EEMD with multiwavelet packet, Hilbert-Huang transform and multiwavelet packet transform for multi-fault diagnosis.