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- Shi, JunchuanDepartment of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA (author)
Planetary gearbox fault diagnosis using bidirectional-convolutional LSTM networks
- Article/chapterEnglish2022
Publisher, publication year, extent ...
- Elsevier,2022
- printrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:ltu-84638
- https://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-84638URI
- https://doi.org/10.1016/j.ymssp.2021.107996DOI
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- Language:English
- Summary in:English
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- Validerad;2021;Nivå 2;2021-05-26 (beamah)
- Gearbox fault diagnosis is expected to significantly improve the reliability, safety and efficiency of power transmission systems. However, planetary gearbox fault diagnosis remains a challenge due to complex responses caused by multiple planetary gears. Model-based gearbox fault diagnosis techniques extract hand-crafted features from sensor data based on underlying physics and statistical analysis, which are not effective in extracting spatial and temporal features automatically. While deep learning methods such as convolutional neural network (CNN) enable automatic feature extraction from multiple sensor sources, they are not capable of extracting spatial and temporal features simultaneously without losing critical feature information. To address this issue, we introduce a novel deep neural network based on bidirectional-convolutional long short-term memory (BiConvLSTM) networks to determine the type, location, and direction of planetary gearbox faults by extracting spatial and temporal features from both vibration and rotational speed measurements automatically and simultaneously. In particular, a CNN determines spatial correlations between two measurements within one time step automatically by combining signals collected from three accelerometers and one tachometer. Long short-term memory (LSTM) networks identify temporal dependencies between two adjacent time steps. By replacing input-to-state and state-to-state operations in the LSTM cell with convolutional operations, the BiConvLSTM can learn spatial correlations and temporal dependencies without losing critical features. Experimental results have shown that the BiConvLSTM network can detect the type, location, and direction of gearbox faults with higher accuracy than conventional deep learning approaches such as CNN, LSTM, and CNN-LSTM.
Subject headings and genre
- NATURVETENSKAP Data- och informationsvetenskap Annan data- och informationsvetenskap hsv//swe
- NATURAL SCIENCES Computer and Information Sciences Other Computer and Information Science hsv//eng
- Planetary gearbox
- Fault diagnosis
- Deep learning
- Spatiotemporal feature
- Drift och underhållsteknik
- Operation and Maintenance
Added entries (persons, corporate bodies, meetings, titles ...)
- Peng, DikangSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia (author)
- Peng, ZhongxiaoSchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia (author)
- Zhang, ZiyangDepartment of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA (author)
- Goebel, KaiLuleå tekniska universitet,Drift, underhåll och akustik,Palo Alto Research Center, Palo Alto, CA 94034, USA(Swepub:ltu)kaigoe (author)
- Wu, DazhongDepartment of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USA (author)
- Department of Mechanical and Aerospace Engineering, University of Central Florida, Orlando, FL 32816, USASchool of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW 2052, Australia (creator_code:org_t)
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- In:Mechanical systems and signal processing: Elsevier1620888-32701096-1216
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