Fault detection of complex planetary gearbox using acoustic signals. (June 2021)

• Record Type: Journal Article
• Authors: Yao, Jiachi; Liu, Chao; Song, Keyu; Zhang, Xiaochen; Jiang, Dongxiang
• Is Part Of: Measurement. Volume 178(2021)
• Publication Date: 2021-06
• Subjects: Acoustic signals -- Planetary gearbox -- Fault detection -- Graph spectral domain -- Spectral graph wavelet transform; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2021.109428
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A supervised adaptive spectral graph wavelet transform (SASGWT) is proposed. The acoustic signal is converted into the graph spectral domain for analysis. An optimization algorithm using combination parameter index is proposed in SGWT. The SASGWT method achieved excellent weak fault detection effect in the test. Abstract: This article describes an acoustic-based fault detection technique of three-stage planetary gearbox in actual engineering applications. The proposed technique was used to extract weak fault signal features. The measured acoustic signals were converted into the path graph from a new perspective in the graph spectral domain. Then an optimized spectral graph wavelet transform method is proposed to decompose the path graph into multiple components. Furthermore, reference signal was constructed based on the structure of planetary gearbox, and the fault-related component is output by comparing the similarity between the constructed reference signal and the decomposed components. Finally, the envelope spectrum analysis is further adopted to identify faults. The proposed supervised adaptive spectral graph wavelet transform (SASGWT) method has better performance than traditional wavelet decomposition method. The weak fault detection effect based on acoustic signals is superior to vibration signals under the experimental conditions of this work.