Time-frequency representation based on robust local mean decomposition for multicomponent AM-FM signal analysis. (October 2017)

• Record Type: Journal Article
• Authors: Liu, Zhiliang; Jin, Yaqiang; Zuo, Ming J.; Feng, Zhipeng
• Is Part Of: Mechanical systems and signal processing. Volume 95(2017)
• Page Start: 468
• Page End: 487
• Publication Date: 2017-10
• Subjects: Time frequency representation -- Local mean decomposition -- Multicomponent AM-FM signal -- Fast kurtogram (spectral kurtosis) -- Machinery fault diagnosis; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2017.03.035
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A new improvement scheme of local mean decomposition is proposed to eliminate the end effect and mode mixing problems. The improved LMD is applied to time-frequency representation for multi-component AM-FM signals. The improved LMD in conjunction with the fast Kurtogram is applied to bearing fault diagnosis. Abstract: Local mean decomposition (LMD) is a promising approach to implement time-frequency representation (TFR) for multicomponent amplitude-modulated (AM) and frequency-modulated (FM) signal analysis; however, its performance usually suffers from end effect and mode mixing problems. To address this issue, this paper proposes a novel comprehensive scheme to improve LMD performance. The novel scheme can automatically determine the fix subset size of the moving average algorithm and the optimal number of sifting iterations in a sifting process. Extensive simulations have been explored for multicomponent AM-FM signal analysis by means of TFR with the improved LMD. Moreover, the improved LMD shows potential application in bearing fault diagnosis in conjunction with the well-known fast kurtogram.