Volume 49 Issue 9
Sep.  2023
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Article Navigation >  Journal of Mine Automation  > 2023  >  49(9): 140-146
LI Bo, GUO Xingran, LI Juanli, et al. A fault warning method for scraper conveyor chain transmission system based on LSTM-Adam[J]. Journal of Mine Automation，2023，49（9）：140-146.  doi: 10.13272/j.issn.1671-251x.18086
Citation: LI Bo, GUO Xingran, LI Juanli, et al. A fault warning method for scraper conveyor chain transmission system based on LSTM-Adam[J]. Journal of Mine Automation，2023，49（9）：140-146.  doi: 10.13272/j.issn.1671-251x.18086
shu

A fault warning method for scraper conveyor chain transmission system based on LSTM-Adam

doi: 10.13272/j.issn.1671-251x.18086
  • 1.

    College of Mechanical and Vehicle Engineering, Taiyuan University of Technology, Taiyuan 030024, China

  • 2.

    Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, Taiyuan 030024, China

  • Received Date: 2023-03-15
  • Rev Recd Date: 2023-09-24
    • Available Online: 2023-09-28
    Abstract
  • The scraper conveyor chain transmission system is prone to frequent faults due to its complex load bearing capacity. However, traditional fault diagnosis requires a large amount of prior knowledge and subjective intervention, which requires high technical personnel. In order to achieve the autonomy, accuracy, and efficiency of fault warning for the scraper conveyor chain transmission system, a fault warning method for the scraper conveyor chain transmission system based on LSTM-Adam is proposed using the powerful data mining capability of deep learning. Firstly, a monitoring system for the working conditions of the scraper conveyor is built based on configuration technology. The system collects real-time operating data of the scraper conveyor, such as the torque and speed of the output shaft of the reducer, the pressure of the middle groove plate, the vibration acceleration in the vertical direction of the scraper, and the strain in the running direction of the scraper chain. The data is cleaned and normalized in min-max to provide data support for fault warning. Secondly, a prediction model is built based on LSTM and trained and optimized using the Adam optimization algorithm to obtain the optimal LSTM Adam prediction model. Finally, the real-time operating data of the scraper conveyor is imported into the LSTM-Adam prediction model to obtain the predicted values of the scraper conveyor operating parameters. The sliding weighted average method is used to calculate the residual between the predicted value and the true value. The maximum residual of the same type of data under normal operating conditions is used as the warning threshold. When the residual exceeds the warning threshold, an early warning is given. The experimental results show that the LSTM-Adam prediction model can accurately predict the trend of strain data of the scraper chain and provide accurate warnings for stuck chain and broken chain faults.

     

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    • References(21)
  • [1]
    于林. 矿用重型刮板输送机断链故障监测传感器研究[J]. 煤炭学报,2011,36(11):1934-1937.

    YU Lin. Research on sensor used to detect chain-broken on armoured face conveyor[J]. Journal of China Coal Society,2011,36(11):1934-1937.
    [2]
    赵驭阳. 基于DAG−SVM的煤矿井下输送装置故障在线检测[J]. 机床与液压,2021,49(10):189-194. doi: 10.3969/j.issn.1001-3881.2021.10.038

    ZHAO Yuyang. On-line fault detection of coal mine underground conveyor device based on DAG-SVM[J]. Machine Tool & Hydraulics,2021,49(10):189-194. doi: 10.3969/j.issn.1001-3881.2021.10.038
    [3]
    崔宏尧,刘敏智. 基于混沌差分进化FCM的刮板输送机故障诊断[J]. 煤矿机械,2020,41(10):172-174.

    CUI Hongyao,LIU Minzhi. Fault diagnosis of scraper conveyor based on chaotic differential evolution FCM[J]. Coal Mine Machinery,2020,41(10):172-174.
    [4]
    LIN Yi,WU Yuankai,GUO Dongyue,et al. A deep learning framework of autonomous pilot agent for air traffic controller training[J]. IEEE Transactions on Human-Machine Systems,2021,51(5):442-450. doi: 10.1109/THMS.2021.3102827
    [5]
    ZHU Xiaoxun,HANG Xinyu,GAO Xiaoxia,et al. Research on crack detection method of wind turbine blade based on a deep learning method[J]. Applied Energy,2022,328. DOI: 10.1016/j.apenergy.2022.120241.
    [6]
    WU Rui,LIU Chao,HAN Te,et al. A planetary gearbox fault diagnosis method based on time-series imaging feature fusion and a transformer model[J]. Measurement Science and Technology,2023,34(2). DOI: 10.1088/1361-6501/ac9e6c.
    [7]
    文成林,吕菲亚. 基于深度学习的故障诊断方法综述[J]. 电子与信息学报,2020,42(1):234-248.

    WEN Chenglin,LYU Feiya. Review on deep learning based fault diagnosis[J]. Journal of Electronics & Information Technology,2020,42(1):234-248.
    [8]
    任建亭,汤宝平,雍彬,等. 基于深度变分自编码网络融合SCADA数据的风电齿轮箱故障预警[J]. 太阳能学报,2021,42(4):403-408.

    REN Jianting,TANG Baoping,YONG Bin,et al. Wind turbine gearbox fault warning based on depth variational autoencoders network fusion SCADA data[J]. Acta Energiae Solaris Sinica,2021,42(4):403-408.
    [9]
    万安平,杨洁,王景霖,等. 基于深度学习的航空发动机齿轮故障诊断[J]. 振动. 测试与诊断,2022,42(6):1062-1067,1239.

    WAN Anping,YANG Jie,WANG Jinglin,et al. Fault diagnosis of aeroengine gear based on deep learning[J]. Journal of Vibration,Measurement & Diagnosis,2022,42(6):1062-1067,1239.
    [10]
    王学文,李素华,谢嘉成,等. 机器人运动学与时序预测融合驱动的刮板输送机调直方法[J]. 煤炭学报,2021,46(2):652-666.

    WANG Xuewen,LI Suhua,XIE Jiacheng,et al. Straightening method of scraper conveyor driven by robot kinematics and time series prediction[J]. Journal of China Coal Society,2021,46(2):652-666.
    [11]
    刘家瑞,杨国田,杨锡运. 基于深度卷积自编码器的风电机组故障预警方法研究[J]. 太阳能学报,2022,43(11):215-223.

    LIU Jiarui,YANG Guotian,YANG Xiyun. Research on wind turbine fault warning method based on deep convolution auto-encoder[J]. Acta Energiae Solaris Sinica,2022,43(11):215-223.
    [12]
    MA Yanhua,DU Xian,SUN Ximing. Adaptive modification of turbofan engine nonlinear model based on LSTM neural networks and hybrid optimization method[J]. Chinese Journal of Aeronautics,2022,35(9):314-332. doi: 10.1016/j.cja.2021.11.005
    [13]
    LI Lei,HASSAN M A,YANG Shurong,et al. Development of image-based wheat spike counter through a faster R-CNN algorithm and application for genetic studies[J]. The Crop Journal,2022,10(5):1303-1311. doi: 10.1016/j.cj.2022.07.007
    [14]
    WU Yizhi,FAN Yiren. Fast hierarchical inversion for borehole resistivity measurements in high-angle and horizontal wells using ADNN-AMLM[J]. Journal of Petroleum Science and Engineering,2021,203. DOI: 10.1016/j.petrol.2021.108662.
    [15]
    LEE J H,HONG J K. Comparative performance analysis of vibration prediction using RNN techniques[J]. Electronics,2022,11(21). DOI: 10.3390/electronics11213619.
    [16]
    杨婷婷,高乾,李浩千,等. 基于卷积神经网络−长短时记忆神经网络的磨煤机故障预警[J]. 热力发电,2022,51(10):122-129.

    YANG Tingting,GAO Qian,LI Haoqian,et al. Coal mill fault early warning technology based on CNN-LSTM network[J]. Thermal Power Generation,2022,51(10):122-129.
    [17]
    王莹莹,安维峥,乔婷婷,等. 基于LSTM的水下电子模块温度预测及预警方法[J]. 中国海上油气,2022,34(1):161-167.

    WANG Yingying,AN Weizheng,QIAO Tingting,et al. LSTM-based temperature prediction and early warning method for subsea electronic module[J]. China Offshore Oil and Gas,2022,34(1):161-167.
    [18]
    HAN Fei,DU Wenhua,ZENG Zhiqiang,et al. A novel dense residual network based on Adam-S optimizer for fault diagnosis of bearings under different working conditions[J]. Measurement Science and Technology,2022,33(12). DOI: 10.1088/1361-6501/ac8dad.
    [19]
    BAFAKEEH O T,YASIR M,RAZA A,et al. The minimality of mean square error in chirp approximation using fractional fourier series and fractional fourier transform[J]. Scientific Reports,2022,12. DOI: 10.1038/s41598-022-23560-8.
    [20]
    LI Shichun,MO Bin,WANG Kunming,et al. Nonlinear prediction modeling of surface quality during laser powder bed fusion of mixed powder of diamond and Ni-Cr alloy based on residual analysis[J]. Optics & Laser Technology,2022,151. DOI: 10.1016/j.optlastec.2022.107980.
    [21]
    ZHEN Dong,GUO Junchao,XU Yuandong,et al. A novel fault detection method for rolling bearings based on non-stationary vibration signature analysis[J]. Sensors,2019,19(18). DOI: 10.3390/s19183994.
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