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Refined composite hierarchical multiscale Lempel-Ziv complexity: A quantitative diagnostic method of multi-feature fusion for rotating energy devices

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  • Zhao, Zhigao
  • Chen, Fei
  • Gui, Zhonghua
  • Liu, Dong
  • Yang, Jiandong

Abstract

Digitalization and intellectualization of energy system require the fault information of energy conversion devices to be captured as accurately as possible from the massive data, so as to realize early fault alert. However, the comprehensiveness of feature extraction, the setting difficulty of model parameters and applicability of various scenarios have shortcomings by conventional methods, leading to their limitations in measured signals with multi-source and multi-feature information. Therefore, this paper exploits a quantitative diagnostic method named refined composite hierarchical multiscale Lempel-Ziv complexity (RCHMLZC). Firstly, the enhanced hierarchical decomposition and multiscale Lempel-Ziv complexity (MLZC) are coupled to develop hierarchical multiscale Lempel-Ziv complexity (HMLZC), which overcomes the drawback of MLZC that cannot quantify the complexity of signals at different frequencies. Secondly, RCHMLZC is proposed to solve the problem that LZC value of HMLZC fluctuates greatly under high scale factors, and then is used to extract the features of vibration signals. Finally, the extracted features are input into the random forests model to realize the efficient recognition of different status signals of rotating energy devices. A total of 14 types of multi-feature fault signals from bearings, shafting and runner are used to verify the reliability and superiority of the proposed method. Compared to the five conventional models, the comprehensive indicators of the proposed method for bearing fault experiments are improved by 1.549%, 4.637%, 14.153%, 20.242% and 22.112%, while the values for the shafting fault experiments are improved by 0.404%, 0.427%, 2.778%, 2.722% and 5.895%. In addition, the proposed method is applied to the analysis of the fault cases of hydraulic turbine, demonstrated the ability to zero miscalculation. It would be a helpful tool to improve energy conversion efficiency and reduce maintenance cost.

Suggested Citation

  • Zhao, Zhigao & Chen, Fei & Gui, Zhonghua & Liu, Dong & Yang, Jiandong, 2023. "Refined composite hierarchical multiscale Lempel-Ziv complexity: A quantitative diagnostic method of multi-feature fusion for rotating energy devices," Renewable Energy, Elsevier, vol. 218(C).
    • Handle: RePEc:eee:renene:v:218:y:2023:i:c:s0960148123012259
    DOI: 10.1016/j.renene.2023.119310
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    References listed on IDEAS

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    1. Zheng, Xianghao & Zhang, Suqi & Zhang, Yuning & Li, Jinwei & Zhang, Yuning, 2023. "Dynamic characteristic analysis of pressure pulsations of a pump turbine in turbine mode utilizing variational mode decomposition combined with Hilbert transform," Energy, Elsevier, vol. 280(C).
    2. Ren, He & Liu, Wenyi & Shan, Mengchen & Wang, Xin & Wang, Zhengfeng, 2021. "A novel wind turbine health condition monitoring method based on composite variational mode entropy and weighted distribution adaptation," Renewable Energy, Elsevier, vol. 168(C), pages 972-980.
    3. Zhu, Zuanyu & Cheng, Junsheng & Wang, Ping & Wang, Jian & Kang, Xin & Yang, Yu, 2023. "A novel fault diagnosis framework for rotating machinery with hierarchical multiscale symbolic diversity entropy and robust twin hyperdisk-based tensor machine," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    4. Fu, Wenlong & Zhang, Kai & Wang, Kai & Wen, Bin & Fang, Ping & Zou, Feng, 2021. "A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM," Renewable Energy, Elsevier, vol. 164(C), pages 211-229.
    5. Xie, Tianming & Xu, Qifa & Jiang, Cuixia & Lu, Shixiang & Wang, Xiangxiang, 2023. "The fault frequency priors fusion deep learning framework with application to fault diagnosis of offshore wind turbines," Renewable Energy, Elsevier, vol. 202(C), pages 143-153.
    6. Cui, Bodi & Weng, Yang & Zhang, Ning, 2022. "A feature extraction and machine learning framework for bearing fault diagnosis," Renewable Energy, Elsevier, vol. 191(C), pages 987-997.
    7. Zhang, Mingyang & Zhang, Di & Fu, Shanshan & Kujala, Pentti & Hirdaris, Spyros, 2022. "A predictive analytics method for maritime traffic flow complexity estimation in inland waterways," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    8. Fu, Wenlong & Fang, Ping & Wang, Kai & Li, Zhenxing & Xiong, Dongzhen & Zhang, Kai, 2021. "Multi-step ahead short-term wind speed forecasting approach coupling variational mode decomposition, improved beetle antennae search algorithm-based synchronous optimization and Volterra series model," Renewable Energy, Elsevier, vol. 179(C), pages 1122-1139.
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