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Research on Fault Diagnosis of Mining Excavator Engine Based on IWOA-LightGBM Model
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Qinghua GU1, 2, 3, Shuyu BAI1, 2, Dan WANG1, 3
Mining Research and Development | 2025, 45(9) : 184 - 191
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Mining Research and Development | 2025, 45(9): 184-191
Research on Fault Diagnosis of Mining Excavator Engine Based on IWOA-LightGBM Model
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Qinghua GU1, 2, 3, Shuyu BAI1, 2, Dan WANG1, 3
Affiliations
  • 1.School of Management, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, China
  • 2.Xi’an Key Laboratory of Perception, Computing and Decision Making for Intelligent Industry, Xi’an, Shaanxi 710055, China
  • 3.School of Resources Engineering, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, China
Published: 2025-09-25
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Aiming at the problem of low fault diagnosis accuracy caused by unbalanced fault categories of mining excavator engine, an intelligent fault diagnosis method of mining excavator engine based on improved whale optimization algorithm (WOA) to optimize light gradient boosting machine (LightGBM) was proposed. Firstly, the recursive feature cross-validation elimination method (RFECV) was used to extract the features of the fault data of collected excavator engine, and the irrelevant features were deleted. Secondly, Focal-Loss was used to improve the loss function of LightGBM, and an improved WOA was proposed to optimize the hyperparameters of LightGBM, and a new diagnostic model was constructed. Finally, the engine fault data of a mine excavator was used for verification, and compared with common integrated models, tuning frameworks and diagnostic algorithms. The results show that the accuracy and F1 score of the proposed mining excavator engine fault diagnosis model IWOA-LightGBM are 98.08% and 98.53% respectively, and the diagnostic performance is good, which can provide reference for the intelligent diagnosis of mining machinery and equipment.

Mining excavator  /  Engine  /  Fault diagnosis  /  Recursive feature cross-validation elimination method  /  Light gradient boosting machine  /  Whale optimization algorithm
顾清华, 白书宇, 王丹. 基于IWOA-LightGBM模型的矿用挖掘机发动机故障诊断研究. 矿业研究与开发, 2025 , 45 (9) : 184 -191 .
Qinghua GU, Shuyu BAI, Dan WANG. Research on Fault Diagnosis of Mining Excavator Engine Based on IWOA-LightGBM Model[J]. Mining Research and Development, 2025 , 45 (9) : 184 -191 .
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Year 2025 volume 45 Issue 9
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Article Info
  • Receive Date：2024-09-29
  • Online Date：2026-10-28
  • Published：2025-09-25
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  • Received：2024-09-29
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Affiliations
    1.School of Management, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, China
    2.Xi’an Key Laboratory of Perception, Computing and Decision Making for Intelligent Industry, Xi’an, Shaanxi 710055, China
    3.School of Resources Engineering, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710055, 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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