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Deep forest based intelligent fault diagnosis of hydraulic turbine

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Abstract

Deep neural networks (DNNs) for intelligent machinery fault diagnosis require a large amount of training data, powerful computational facilities and have many hyper-parameters that have to be carefully tuned to ensure maximum performance. Deep forest, as a novel alternative to the deep learning framework, has the potential to overcome these shortcomings. In this study, a deep forest-based end-to-end intelligent fault diagnosis method is proposed for hydraulic turbine, in which multi-grained scanning is first used to transform fault feature representations from raw data and enhance fault feature learning ability, and then cascade structure is constructed with different types of random forests to learn fault features level by level and classify faults. The effectiveness of the proposed method is validated using the experimental dataset under twelve conditions, and its practicability is validated using a simulated dataset generated by adding white Gaussian noise to raw experimental signals. The results show that the proposed method is able to adaptively mine available fault features from measured signals, and its diagnosis accuracy is better than that obtained by existing methods. More importantly, the proposed method has better robustness to noise and is less limited to the number of training data.

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Author information

Authors and Affiliations

  1. College of Water Resource and Hydropower, Sichuan University, Chengdu, 610065, China

    Xiaolian Liu & Hao Wang

  2. State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing, 100038, China

    Yu Tian, Xiaohui Lei & Hao Wang

  3. China Eastern Route Corporation of South-to-North Water Diversion, Beijing, 100029, China

    Mei Liu

  4. College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, 210098, China

    Xin Wen

  5. School of Power and Mechanical Engineering, Wuhan University, Wuhan, 430072, China

    Haocheng Huang

Authors
  1. Xiaolian Liu
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  2. Yu Tian
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  3. Xiaohui Lei
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  4. Mei Liu
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  5. Xin Wen
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  6. Haocheng Huang
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  7. Hao Wang
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Corresponding author

Correspondence to Yu Tian.

Additional information

Recommended by Associate Editor Doo Ho Lee

Xiaolian Liu is a Ph.D. candidate in Sichuan University, who received the B.E. and M.E in hydraulic engineering from the Sichuan University, in 2013 and 2016, respectively. Her current research interests include hydraulic simulation, artificial intelligence and fault diagnosis.

Yu Tian is a Senior Engineer of China Institute of Water Resources and Hydropower Research (IWHR), who received the B.E. degree, M.E and Ph.D. in hydraulic engineering from the Tianjin University, Tianjin, China, in 2007, 2009 and 2012, respectively. She is with The China Institute of Water Resources and Hydropower research since 2012. Her current research interests include intelligent water resources management, hydraulic simulation, water resources regulation, big data analysis, and artificial intelligence.

Xiaohui Lei earned his Ph.D. from University of Tsukuba in water environment restoration in 2007. He now works in the Water Resources Department of IWHR as a Professorate Senior Engineer and the Director of Water Resources Regulation Office. Since then, Lei’s researches are focused on the operation of reservoirs and inter basin water diversions.

Mei Liu is a Senior Engineer of China Eastern Route Corporation of South-to-North Water Diversion, with a Ph.D. in Environmental science from the Beijing Normal University, Beijing, China, in 2012. She has been in Tsinghua University postdoctoral research station in 2013–2016. Her current research interests include water resources management, water resources regulation, and water environment management.

Xin Wen is currently an Associate Professor at the College of Water Conservancy and Hydropower Engineering at Hohai University. His research and technology transfer activities include multiple water projects operation, climate changes assessment and adaption, wind-solar-hydro hybrid energy system planning and optimization.

Wang Hao finished his Ph.D. at Tsinghua University in 1989. He is an eminent expert in hydrology and water resources, He currently heads the State Key Laboratory of Water Cycle Simulation and Regulation, and Department of Water Resources, China Institute of Water Resources and Hydropower Research.

Haocheng Huang is a Master’s candidate at Wuhan University. His current research interests are fluid machinery and hydraulic transient.

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Liu, X., Tian, Y., Lei, X. et al. Deep forest based intelligent fault diagnosis of hydraulic turbine. J Mech Sci Technol 33, 2049–2058 (2019). https://doi.org/10.1007/s12206-019-0408-9

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  • DOI: https://doi.org/10.1007/s12206-019-0408-9

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