IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i9p1301-d1382720.html
   My bibliography  Save this article

Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems

Author

Listed:
  • Jia Tian

    (Cangdong Power Plant, Cangzhou 061113, China
    Transportation College, Northeast Forestry University, Harbin 150040, China)

  • Xingqin Zhang

    (Transportation College, Northeast Forestry University, Harbin 150040, China)

  • Shuangqing Zheng

    (Cangdong Power Plant, Cangzhou 061113, China
    Transportation College, Northeast Forestry University, Harbin 150040, China)

  • Zhiyong Liu

    (Cangdong Power Plant, Cangzhou 061113, China)

  • Changshu Zhan

    (Transportation College, Northeast Forestry University, Harbin 150040, China)

Abstract

In the realm of automated industry and smart production, the deployment of fault warning systems is crucial for ensuring equipment reliability and enhancing operational efficiency. Although there are a multitude of existing methodologies for fault warning, the proficiency of these systems in processing and analysing data is increasingly challenged by the progression of industrial apparatus and the escalating magnitude and intricacy of the data involved. To address these challenges, this research outlines an innovative fault warning methodology that combines a bi-directional long short-term memory (Bi-LSTM) network with an enhanced hunter–prey optimisation (EHPO) algorithm. The Bi-LSTM network is strategically utilised to outline complex temporal patterns in machinery operational data, while the EHPO algorithm is employed to meticulously fine-tune the hyperparameters of the Bi-LSTM, aiming to enhance the accuracy and generalisability of fault warning. The EHPO algorithm, building upon the foundational hunter–prey optimisation (HPO) framework, introduces an advanced population initialisation process, integrates a range of strategic exploration methodologies, and strengthens its search paradigms through the incorporation of the differential evolution (DE) algorithm. This comprehensive enhancement aims to boost the global search efficiency and accelerate the convergence speed of the algorithm. Empirical analyses, conducted using datasets from real-world industrial scenarios, have validated the improved warning performance of this proposed methodology against some benchmark techniques, as evidenced by superior metrics such as root mean square error (RMSE) and mean absolute error (MAE), albeit with a slight increase in computational resource requirements. This study not only proposes a novel paradigm for fault warning within complex industrial frameworks but also contributes to the discourse on hyperparameter optimisation within the field of machine learning algorithms.

Suggested Citation

  • Jia Tian & Xingqin Zhang & Shuangqing Zheng & Zhiyong Liu & Changshu Zhan, 2024. "Synergising an Advanced Optimisation Technique with Deep Learning: A Novel Method in Fault Warning Systems," Mathematics, MDPI, vol. 12(9), pages 1-25, April.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1301-:d:1382720
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/9/1301/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/9/1301/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhao, Hongshan & Liu, Huihai & Hu, Wenjing & Yan, Xihui, 2018. "Anomaly detection and fault analysis of wind turbine components based on deep learning network," Renewable Energy, Elsevier, vol. 127(C), pages 825-834.
    2. Shen, Sheng & Sadoughi, Mohammadkazem & Li, Meng & Wang, Zhengdao & Hu, Chao, 2020. "Deep convolutional neural networks with ensemble learning and transfer learning for capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 260(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Yajun & Liu, Yajie & Wang, Jia & Zhang, Tao, 2022. "State-of-health estimation for lithium-ion batteries by combining model-based incremental capacity analysis with support vector regression," Energy, Elsevier, vol. 239(PB).
    2. Wang, Anqi & Pei, Yan & Qian, Zheng & Zareipour, Hamidreza & Jing, Bo & An, Jiayi, 2022. "A two-stage anomaly decomposition scheme based on multi-variable correlation extraction for wind turbine fault detection and identification," Applied Energy, Elsevier, vol. 321(C).
    3. Wang, Yixiu & Zhu, Jiangong & Cao, Liang & Gopaluni, Bhushan & Cao, Yankai, 2023. "Long Short-Term Memory Network with Transfer Learning for Lithium-ion Battery Capacity Fade and Cycle Life Prediction," Applied Energy, Elsevier, vol. 350(C).
    4. Hu, Chunsheng & Ma, Liang & Guo, Shanshan & Guo, Gangsheng & Han, Zhiqiang, 2022. "Deep learning enabled state-of-charge estimation of LiFePO4 batteries: A systematic validation on state-of-the-art charging protocols," Energy, Elsevier, vol. 246(C).
    5. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    6. Li, Yihuan & Li, Kang & Liu, Xuan & Li, Xiang & Zhang, Li & Rente, Bruno & Sun, Tong & Grattan, Kenneth T.V., 2022. "A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements," Applied Energy, Elsevier, vol. 325(C).
    7. Cao, Mengda & Zhang, Tao & Liu, Yajie & Zhang, Yajun & Wang, Yu & Li, Kaiwen, 2022. "An ensemble learning prognostic method for capacity estimation of lithium-ion batteries based on the V-IOWGA operator," Energy, Elsevier, vol. 257(C).
    8. Mingzhu Tang & Wei Chen & Qi Zhao & Huawei Wu & Wen Long & Bin Huang & Lida Liao & Kang Zhang, 2019. "Development of an SVR Model for the Fault Diagnosis of Large-Scale Doubly-Fed Wind Turbines Using SCADA Data," Energies, MDPI, vol. 12(17), pages 1-15, September.
    9. Li, Penghua & Zhang, Zijian & Grosu, Radu & Deng, Zhongwei & Hou, Jie & Rong, Yujun & Wu, Rui, 2022. "An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    10. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    11. Feng, Chenlong & Liu, Chao & Jiang, Dongxiang, 2023. "Unsupervised anomaly detection using graph neural networks integrated with physical-statistical feature fusion and local-global learning," Renewable Energy, Elsevier, vol. 206(C), pages 309-323.
    12. Zhang, Chen & Yang, Tao, 2021. "Optimal maintenance planning and resource allocation for wind farms based on non-dominated sorting genetic algorithm-ΙΙ," Renewable Energy, Elsevier, vol. 164(C), pages 1540-1549.
    13. Tian, Yong & Dong, Qianyuan & Tian, Jindong & Li, Xiaoyu & Li, Guang & Mehran, Kamyar, 2023. "Capacity estimation of lithium-ion batteries based on optimized charging voltage section and virtual sample generation," Applied Energy, Elsevier, vol. 332(C).
    14. Yang, Dan & Peng, Xin & Ye, Zhencheng & Lu, Yusheng & Zhong, Weimin, 2021. "Domain adaptation network with uncertainty modeling and its application to the online energy consumption prediction of ethylene distillation processes," Applied Energy, Elsevier, vol. 303(C).
    15. Adaiton Oliveira-Filho & Ryad Zemouri & Philippe Cambron & Antoine Tahan, 2023. "Early Detection and Diagnosis of Wind Turbine Abnormal Conditions Using an Interpretable Supervised Variational Autoencoder Model," Energies, MDPI, vol. 16(12), pages 1-21, June.
    16. Xiaolu Wei & Yubo Tian & Na Li & Huanxin Peng, 2024. "Evaluating ensemble learning techniques for stock index trend prediction: a case of China," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 23(3), pages 505-530, September.
    17. Kong, Jin-zhen & Yang, Fangfang & Zhang, Xi & Pan, Ershun & Peng, Zhike & Wang, Dong, 2021. "Voltage-temperature health feature extraction to improve prognostics and health management of lithium-ion batteries," Energy, Elsevier, vol. 223(C).
    18. Alan Turnbull & Conor McKinnon & James Carrol & Alasdair McDonald, 2022. "On the Development of Offshore Wind Turbine Technology: An Assessment of Reliability Rates and Fault Detection Methods in a Changing Market," Energies, MDPI, vol. 15(9), pages 1-20, April.
    19. Lyu, Guangzheng & Zhang, Heng & Miao, Qiang, 2023. "An interpretable state of health estimation method for lithium-ion batteries based on multi-category and multi-stage features," Energy, Elsevier, vol. 283(C).
    20. Wang, Zhe & Yang, Fangfang & Xu, Qiang & Wang, Yongjian & Yan, Hong & Xie, Min, 2023. "Capacity estimation of lithium-ion batteries based on data aggregation and feature fusion via graph neural network," Applied Energy, Elsevier, vol. 336(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:12:y:2024:i:9:p:1301-:d:1382720. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.