Fault diagnosis of rotor using EMD thresholding-based de-noising combined with probabilistic neural network

• Main Authors: Dong Liu, Hongtao Zeng, Zhihuai Xiao, Lihong Peng, O. P. Malik
• Format: Article
• Language: English
• Published: JVE International 2017-12-01
• Series: Journal of Vibroengineering
• Subjects: EMD thresholding de-noising; probabilistic neural-network; fault diagnosis; feature-extract; wavelet de-noising
• Online Access: https://www.jvejournals.com/article/18365

De-noising of signal processing is crucial for fault diagnosis in order to successfully conduct feature extraction and is an efficient method for accurate determination of cause. In this paper, the empirical mode decomposition (EMD) thresholding-based de-noising method and probabilistic neural network (PNN) are respectively used in the de-noising of the vibration signal and rotor fault diagnosis and compared with wavelet thresholding-based de-noising technology and back propagation neural network (BPNN). The results show that the clear iterative EMD interval thresholding performs better than wavelet thresholding in the de-noising of the vibration signal, and avoids the determination of wavelet basis and decomposition level. In addition, the PNN created by feature samples does not require training and has a higher accuracy than BPNN.