Volume 49 Issue 10
Oct.  2023
Turn off MathJax
Article Contents
Article Navigation >  Journal of Mine Automation  > 2023  >  49(10): 75-86
WANG Chen, YANG An. Research on the health evaluation and prediction system for mine hoists[J]. Journal of Mine Automation，2023，49（10）：75-86.  doi: 10.13272/j.issn.1671-251x.2023030092
Citation: WANG Chen, YANG An. Research on the health evaluation and prediction system for mine hoists[J]. Journal of Mine Automation，2023，49（10）：75-86.  doi: 10.13272/j.issn.1671-251x.2023030092
shu

Research on the health evaluation and prediction system for mine hoists

doi: 10.13272/j.issn.1671-251x.2023030092

  • School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, China

  • Received Date: 2023-03-28
  • Rev Recd Date: 2023-10-15
    • Available Online: 2023-10-24
    Abstract
  • In response to the relatively limited research on health evaluation and prediction of the entire system of mine hoists, a health evaluation index system and comment set for mine hoists have been established. The health evaluation and prediction system for mine hoists has been designed. A fuzzy comprehensive evaluation method for the health of mine hoists is proposed to address the issues of insufficient utilization of monitoring data from various components of mine hoists, and the inability of health evaluation results to meet actual production needs. The method introduces relative degradation degree to characterize the health of different types of indicators of the hoist. The method uses health degree to quantify the health of mine hoists. The fuzzy comprehensive evaluation method is used to calculate the health of mine hoists. The analytic hierarchy process (AHP) is improved by replacing the 1-9 scale with an exponential scale to reduce computational complexity. The method uses CRITIC objective weighting method and combines subjective and objective weights to calculate the comprehensive weights of each subsystem and indicator. Based on the fuzzy comprehensive evaluation calculation process and the maximum membership principle, the health evaluation results and fault causes of the mine hoist are obtained. On the basis of the health evaluation results of the mine hoist, the Harris hawks (HHO) algorithm is used to optimize the important parameters of the support vector regression (SVR) model. The HHO-SVR model is constructed to predict the health of the mine hoist, improving the accuracy of the health prediction results. The experimental results show that the fuzzy comprehensive evaluation method can accurately evaluate the health of the hoist. Compared with particle swarm optimization support vector regression (PSO-SVR), genetic algorithm optimization support vector regression (GA-SVR), and grey wolf algorithm optimization support vector regression (GWO-SVR) models, the prediction results of the HHO-SVR model are closer to the actual values and have better prediction performance.

     

    • FullText(HTML)
  • loading
    • References(30)
  • [1]
    WANG Feng,HE Fengyou. Study of hoist perception system based on IOT technology[C]. International Conference on Web Information Systems and Mining,Sanya,2010:357-360.
    [2]
    ZHAO Huadong,WANG Hezheng,LIU Guoning,et al. The application of Internet of things (IOT) technology in the safety monitoring system for hoisting machines[J].Applied Mechanics and Materials,2012, 1976(209/210/211):2142-2145.
    [3]
    LI Juanli,XIE Jiacheng,YANG Zhaojian,et al. Fault diagnosis method for a mine hoist in the Internet of things environment[J]. Sensors,2018,18(6). DOI: 10.3390/s18061920.
    [4]
    LEI Yaguo,LI Naipeng,GUO Liang,et al. Machinery health prognostics:a systematic review from data acquisition to RUL prediction[J]. Mechanical Systems and Signal Processing,2018,104(1):799-834.
    [5]
    PENG Ying,DONG Ming. A prognosis method using age-dependent hidden semi-Markov model for equipment health prediction[J]. Signal Process,2011,25(1):237-252.
    [6]
    LI Hong,PAN Donghui,CHEN C L P. Intelligent prognostics for battery health monitoring using the mean entropy and relevance vector machine[J]. IEEE Transactions on Systems,Man,and Cybernetics:Systems,2014,44(7):851-862.
    [7]
    MA Meng,CHEN Xuefeng,WANG Shibin,et al. Bearing degradation assessment based on weibull distribution and deep belief network[C]. International Symposium on Flexible Automation,Cleveland,2016:382-385.
    [8]
    FUSTER-PARRA P,TAULER P,BENNASAR-VENY M,et al. Bayesian network modeling:a case study of an epidemiologic system analysis of cardiovascular risk[J]. Computer Methods & Programs in Biomedicine,2016,126(12):128-142.
    [9]
    WANG Lanjing,ALI Y,NAZIR S,et al. ISA evaluation framework for security of Internet of health things system using AHP-TOPSIS methods[J]. IEEE Access,2020,8:152316-152332. doi: 10.1109/ACCESS.2020.3017221
    [10]
    MU Tongna,YU Hongmin,ZHANG Xueyan. Research on equipment health assessment based on grey system theory[C]. IEEE Prognostics and System Health Management Conference,Beijing,2012:1-4.
    [11]
    WEI Xiao,LUO Xiangfeng,LI Qing,et al. Online comment-based hotel quality automatic assessment using improved fuzzy comprehensive evaluation and fuzzy cognitive map[J]. IEEE Transactions on Fuzzy Systems,2015,23(1):72-84. doi: 10.1109/TFUZZ.2015.2390226
    [12]
    LI Juanjuan,MENG Guoying,XIE Guangming,et al. Study on health assessment method of a braking system of a mine hoist[J]. Sensors,2019,19(4). DOI: 10.3390/s19040769.
    [13]
    DE SANTO A,GALLI A,GRAVINA M,et al. Deep learning for HDD health assessment:an application based on LSTM[J]. IEEE Transactions on Computers,2022,71(1):69-80. doi: 10.1109/TC.2020.3042053
    [14]
    VATANI M,SZEREPKO M,PREBEN VIE J S. State of health prediction of li-ion batteries using incremental capacity analysis and support vector regression[C]. IEEE Milan PowerTech,Milan,2019:1-6. DOI: 10.1109/PTC.2019.8810665.
    [15]
    DEY P,CHAULYA S K,KUMAR S. Hybrid CNN-LSTM and IoT-based coal mine hazards monitoring and prediction system[J]. Process Safety and Environmental Protection,2021,152:249-263. doi: 10.1016/j.psep.2021.06.005
    [16]
    CHENG Hongju,XIE Zhe,SHI Yushi,et al. Multi-step data prediction in wireless sensor networks based on one-dimensional CNN and bidirectional LSTM[J]. IEEE Access,2019,7:117883-117896. doi: 10.1109/ACCESS.2019.2937098
    [17]
    REN Lei,DONG Jiabao,WANG Xiaokang,et al. A data-driven Auto-CNN-LSTM prediction model for lithium-ion battery remaining useful life[J]. IEEE Transactions on Industrial Informatics,2021,17(5):3478-3487. doi: 10.1109/TII.2020.3008223
    [18]
    QIN Taichun,ZENG Shengkui,GUO Jianbin. Robust prognostics for state of health estimation of lithium-ion batteries based on an improved PSO-SVR model[J]. Microelectronics Reliability,2015,55(9/10):1280-1284.
    [19]
    MA Jun,TENG Zhaosheng,TANG Qiu,et al. Measurement error prediction of power metering equipment using improved local outlier factor and kernel support vector regression[J]. IEEE Transactions on Industrial Electronics,2022,69(9):9575-9585. doi: 10.1109/TIE.2021.3114740
    [20]
    SAIDI L,ALI J B,BECHHOEFER E,et al. Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR[J]. Applied Acoustics,2017,120:1-8. doi: 10.1016/j.apacoust.2017.01.005
    [21]
    FU Wenlong,SHAO Kaixuan,TAN Jiawen,et al. Fault diagnosis for rolling bearings based on composite multiscale fine-sorted dispersion entropy and SVM with hybrid mutation SCA-HHO algorithm optimization[J]. IEEE Access,2020,8:13086-13104. doi: 10.1109/ACCESS.2020.2966582
    [22]
    AHMED R,ZAYED T,NASIRI F. A hybrid genetic algorithm-based fuzzy Markovian model for the deterioration modeling of healthcare facilities[J]. Algorithms,2020,13(9):210-230. doi: 10.3390/a13090210
    [23]
    HUANG Zhi'an,LE Tian,GAO Yukun,et al. Safety assessment of emergency training for industrial accident scenarios based on analytic hierarchy process and gray-fuzzy comprehensive assessment[J]. IEEE Access,2020,8:144767-144777. doi: 10.1109/ACCESS.2020.3013671
    [24]
    YU Xueyi,MU Chi,ZHANG Dongdong. Assessment of land reclamation benefits in mining areas using fuzzy comprehensive evaluation[J]. Sustainability,2020,12(5):1-20.
    [25]
    VAN HOUDT B. Randomized work stealing versus sharing in large-scale systems with nonexponential job sizes[J]. IEEE/ACM Transactions on Networking,2019,27(5):2137-2149. doi: 10.1109/TNET.2019.2939040
    [26]
    CHITSAZAN M A,FADALI M S,TRZYNADLOWSKI A M. State estimation for large-scale power systems and FACTS devices based on spanning tree maximum exponential absolute value[J]. IEEE Transactions on Power Systems,2020,35(1):238-248. doi: 10.1109/TPWRS.2019.2934705
    [27]
    LIN Zhenzhi,WEN Fushuan,WANG Huifang,et al. CRITIC-based node importance evaluation in skeleton-network reconfiguration of power grids[J]. IEEE Transactions on Circuits and Systems II:Express Briefs,2018,65(2):206-210.
    [28]
    YANG Tingfang,LIU Haifeng,ZENG Xiangjun,et al. Application of a combined decision model based on optimal weights in incipient faults diagnosis for power transformer[J]. IEEJ Transactions on Electrical and Electronic Engineering,2017,12(2):169-175. doi: 10.1002/tee.22363
    [29]
    GUO Yanhui,HAN Siming,SHEN Chuanhe,et al. An adaptive SVR for high-frequency stock price forecasting[J]. IEEE Access,2018,6:11397-11404. doi: 10.1109/ACCESS.2018.2806180
    [30]
    ZHANG Pengcheng,ZHOU Xuewu,PELLICCIONE P,et al. RBF-MLMR:a multi-label metamorphic relation prediction approach using RBF neural network[J]. IEEE Access,2017,5:21791-21805. doi: 10.1109/ACCESS.2017.2758790
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(10)  / Tables(7)

    Get Citation
    PDF
    XML

    Article Metrics

    Article views (124) PDF downloads(28) Cited by()
    Proportional views
    Related

    苏ICP备 05016349号-2

    Supported by: Beijing Renhe Information Technology Co. Ltd.  Visits：    

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return