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Development and Application of Intelligent Operation and Maintenance System for Mining Truck Drive System Based on Data-Driven
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Hui HE1, 2, Peng ZENG1, 2, Yongqiang ZHOU3, Zhouchao LIU1, Xiaozhen XU4
Mining Research and Development | 2025, 45(6) : 207 - 218
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Mining Research and Development | 2025, 45(6): 207-218
Development and Application of Intelligent Operation and Maintenance System for Mining Truck Drive System Based on Data-Driven
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Hui HE1, 2, Peng ZENG1, 2, Yongqiang ZHOU3, Zhouchao LIU1, Xiaozhen XU4
Affiliations
  • 1.School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
  • 2.Jiangxi Provincial Key Laboratory of Low-Carbon Processing and Utilization of Strategic Metal Mineral Resources, Ganzhou, Jiangxi 341000, China
  • 3.School of Physics and Materials Science, Nanchang University, Nanchang, Jiangxi 330031, China
  • 4.The 10th Research Institute of China Aerospace Science and Industry Corporation, Guiyang, Guizhou 550009, China
Published: 2025-06-25
Outline
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Aiming at the problem of difficult diagnosis of mining truck drive system, an intelligent fault diagnosis model was proposed by combining wavelet packet and ensemble empirical mode decomposition (EEMD) method, which was integrated into intelligent operation and maintenance system and applied to mine truck. Firstly, the wavelet packet was used to denoise the vibration signals of bearings and gears in the drive system. Then the EEMD algorithm was used to decompose the signal into several intrinsic mode function (IMF) components, and the feature vector was extracted from the IMF components containing the main state information. Secondly, the support vector machine (SVM) was used to classify the fault modes. The penalty parameter C and kernel function parameter h of SVM were optimized by particle swarm optimization (PSO), genetic algorithm (GA) and multi-population genetic algorithm (MPGA). Four fault diagnosis models of SVM, PSO-SVM, GA-SVM and MPGA-SVM were constructed respectively. It is concluded that MPGA-SVM is the optimal fault diagnosis model, and the diagnostic accuracy of bearing fault and gear fault is the highest, which is 98.75% and 6.67% respectively. Finally, aiming at the RE1060 drive system of XDE320 electric-driven mining dump truck, an intelligent operation and maintenance system including four modules of signal acquisition and storage, time-frequency analysis, fault feature extraction and intelligent fault diagnosis was developed by MATLAB and LabVIEW. The research shows that the intelligent operation and maintenance system can realize on-line monitoring and diagnosis of existing faults, which is of great significance to improve the safe and efficient operation of mining trucks.

Intelligent mining  /  Mining truck  /  Drive system  /  Intelligent operation and maintenance  /  Support vector machine
何慧, 曾鹏, 周永强, 刘周超, 徐晓贞. 基于数据驱动的矿用卡车驱动系统智能运维系统研发与应用. 矿业研究与开发, 2025 , 45 (6) : 207 -218 .
Hui HE, Peng ZENG, Yongqiang ZHOU, Zhouchao LIU, Xiaozhen XU. Development and Application of Intelligent Operation and Maintenance System for Mining Truck Drive System Based on Data-Driven[J]. Mining Research and Development, 2025 , 45 (6) : 207 -218 .
Year 2025 volume 45 Issue 6
47
22
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Article Info
  • Receive Date:2024-07-23
  • Online Date:2026-10-30
  • Published:2025-06-25
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  • Received:2024-07-23
Funding
Affiliations
    1.School of Resources and Environmental Engineering, Jiangxi University of Science and Technology, Ganzhou, Jiangxi 341000, China
    2.Jiangxi Provincial Key Laboratory of Low-Carbon Processing and Utilization of Strategic Metal Mineral Resources, Ganzhou, Jiangxi 341000, China
    3.School of Physics and Materials Science, Nanchang University, Nanchang, Jiangxi 330031, China
    4.The 10th Research Institute of China Aerospace Science and Industry Corporation, Guiyang, Guizhou 550009, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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