@inproceedings{de37aecd85064177b4f92d2d3c2ae9d5,
title = "Repetitive Transient Extraction Algorithm for the Fault Diagnosis of Planetary Gearbox via Encoder Signal",
abstract = "This paper proposes a systematic framework for the fault detection and condition monitoring of planetary gearbox using internal encoder signal rather than traditional external vibration signal. In this work, the raw encoder signal is firstly converted into instantaneous angular speed signal through difference method. Then comb filtering is applied to remove the interferences and highlight the concerned feature components. Finally, sparsity-based signal decomposition algorithm is introduced to separate the fault transients and harmonic components. With the proposed method, the periodical fault transients are successfully extracted and the weak incipient fault can be effectively detected. Moreover, the validity of the proposed method is confirmed through the synthetic encoder signal and real data acquired from the planetary gearbox.",
keywords = "Encoder signal, Fault diagnosis, Planetary gearbox, Repetitive transient extraction",
author = "Chuancang Ding and Ming Zhao and Jing Lin and Kaixuan Liang and Jinyang Jiao",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 32nd International Congress and Exhibition on Condition Monitoring and Diagnostic Engineering Management, COMADEM 2019 ; Conference date: 03-09-2019 Through 05-09-2019",
year = "2020",
doi = "10.1007/978-3-030-57745-2\_67",
language = "英语",
isbn = "9783030577445",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "811--825",
editor = "Andrew Ball and Len Gelman and B.K.N. Rao",
booktitle = "Advances in Asset Management and Condition Monitoring, COMADEM 2019",
}