Cepstrum-assisted empirical wavelet transform (CEWT)-based improved demodulation analysis for fault diagnostics of planetary gearboxes. (October 2021)

• Record Type: Journal Article
• Authors: Kim, Yunhan; Ha, Jong M.; Na, Kyumin; Park, Jungho; Youn, Byeng D.
• Is Part Of: Measurement. Volume 183(2021)
• Publication Date: 2021-10
• Subjects: Fault diagnostics -- Planetary gearbox -- Vibration signal -- Empirical wavelet transform (EWT) -- Cepstrum analysis -- Demodulation analysis; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109796
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A cepstrum-assisted empirical wavelet transform (CEWT) is proposed for fault diagnostics. Cepstrum is adopted to identify the modulation sideband clusters around gear mesh harmonics. Envelope averaging is proposed to improve demodulation analysis for fault diagnostics. Simulation and experimental results demonstrate the superiority of the proposed method for fault diagnostics. Abstract: Demodulation analysis is a widely used approach for fault diagnostics of planetary gearboxes by identifying the fault-induced modulation effect buried in noise with complicated characteristics. To enhance the performance of demodulation analysis, previous studies have employed signal decomposition, including empirical wavelet transform (EWT), to decompose a signal with a clear modulation effect. However, EWT requires a physical understanding of the modulation effect to isolate the fault-related signals. To solve this challenge, we propose a cepstrum-assisted empirical wavelet transform (CEWT). In the proposed method, the vibration signal is decomposed using empirical wavelet filters designed based on the smoothed spectrum from cepstrum analysis. To further enhance the fault-related signal, the proposed method employs averaging for the envelopes of the decomposed signals for the demodulation analysis. The proposed method is validated by examining numerical simulation and experiment. The results show that the proposed method improves fault diagnostic performance, as compared to existing methods.