An improved local mean decomposition method based on improved composite interpolation envelope and its application in bearing fault feature extraction. (February 2020)

• Record Type: Journal Article
• Authors: Li, Xiang; Ma, Jun; Wang, Xiaodong; Wu, Jiande; Li, Zhuorui
• Is Part Of: ISA transactions. Volume 97(2020)
• Page Start: 365
• Page End: 383
• Publication Date: 2020-02
• Subjects: Improved local mean decomposition -- Improved compound interpolation envelope -- Hilbert demodulation -- Kurtosis criterion -- Fault feature extraction -- Rolling bearings; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2019.07.027
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: In order to overcome the influence of non-adaptive selection of non-stationary coefficient threshold of compound interpolation envelope (CIE) method on decomposition performance of local mean decomposition (LMD), a LMD method based on improved compound interpolation envelope (ICIE) is proposed in this paper. Firstly, combining the CIE with fractal box dimension, an improved envelope processing method, named ICIE, is proposed. Secondly, an improved LMD-based ICIE is presented and abbreviated as ICIELMD. Finally, three different data-sets, including simulation signal, rolling bearing data-sets from Case Western Reserve University (CWRU) and National Aeronautics and Space Administration (NASA), are used to complete the comparative experiments between the proposed ICIELMD and state-of-the-art methods (CIELMD) and demonstrate the effectiveness of the ICIELMD method. The experimental results show that the proposed method achieves comparable or slightly better than the other methods, and provides a new solution for complex signal analysis of rolling bearing faults. Highlights: The improved composite interpolation envelope method based on the combination of CIE and fractal box dimension is proposed. An fault feature extraction method of rolling bearings combined ICIELMD with kurtosis criterion is presented. The validity of the presented ICIELMD method is verified by the experimental cases of three different data-sets.