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Fault Diagnosis of Roadheader Cutting Head Based on Improved RCMDE and Optimised Random Forests
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Tian-bing MA1, 2, 3, Ting YANG1, Chang-peng LI1, 2, 3, Fei DU1, 3, *, Rui SHI1, 2, 3, Ping-ping YU1
Science Technology and Engineering | 2025, 25(9) : 3629 - 3636
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Science Technology and Engineering | 2025, 25(9): 3629-3636
• Papers·Mining and Metallurgical Engineering •
Fault Diagnosis of Roadheader Cutting Head Based on Improved RCMDE and Optimised Random Forests
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Tian-bing MA1, 2, 3, Ting YANG1, Chang-peng LI1, 2, 3, Fei DU1, 3, *, Rui SHI1, 2, 3, Ping-ping YU1
Affiliations
  • 1 State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal, Anhui University of Science and Technology, Huainan 232001, China
  • 2 Institute of Energy, Hefei Comprehensive National Science Center (Anhui Energy Laboratory), Hefei 230051, China
  • 3 School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China
Published: 2025-03-28 doi: 10.12404/j.issn.1671-1815.2403688
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To address the challenges of extracting and identifying fault features from roadheader cutting vibration signal, a new fault diagnosis method of roadheader cutting head based on the refine composite multi-scale fuzzy dispersion entropy(RCMFDE) and hippo optimized random forest(HORF) was proposed. Firstly, RCMFDE was used to comprehensively characterize the fault feature information of the roadheader cutting head, and the fault feature data set was constructed. Secondly, the fault type was trained and tested by the HORF to realize the fault pattern recognition of the cutting head of the roadheader. Finally, the proposed method was applied to the experimental data analysis of the cutting head of the roadheader, and compared with the existing multi-scale fuzzy entropy and fine-complex multi-scale spread entropy fault feature extraction methods. The results of the trial indicate that RCMFDE performs better than the other two entropy approaches in discovering defect features, and hippo random forest outperforms extreme learning machine and support vector machine in error recognition. The fault diagnosis method can more correctly recognize the error type of the cutting head of the roadheader, and the rate of accuracy of the recognition obtained 100%.

roadheader  /  cutting vibration signal  /  feature extraction  /  fault diagnosis  /  fine composite multiscale fuzzy scatter entropy
Tian-bing MA, Ting YANG, Chang-peng LI, Fei DU, Rui SHI, Ping-ping YU. Fault Diagnosis of Roadheader Cutting Head Based on Improved RCMDE and Optimised Random Forests[J]. Science Technology and Engineering, 2025 , 25 (9) : 3629 -3636 . DOI: 10.12404/j.issn.1671-1815.2403688
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Year 2025 volume 25 Issue 9
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Article Info
doi: 10.12404/j.issn.1671-1815.2403688
  • Receive Date：2024-05-18
  • Online Date：2025-07-09
  • Published：2025-03-28
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History
  • Received：2024-05-18
  • Revised：2024-12-27
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Affiliations
    1 State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal, Anhui University of Science and Technology, Huainan 232001, China
    2 Institute of Energy, Hefei Comprehensive National Science Center (Anhui Energy Laboratory), Hefei 230051, China
    3 School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China
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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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