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A fault identification method for cutting head of the roadheader based on parameter optimization VMD and RCMFDE

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Abstract

Cutting head is a part of the roadheader prone to failure. Its health monitoring and fault diagnosis can ensure the safe and efficient operation of the roadheader. The cutting head has been under the complex working condition of variable load for a long time. Therefore, the vibration signal of the cutting head has non-linear time-varying modulation characteristics, which causes serious interference to the fault identification of the cutting head. Hence, this study proposes a feature extraction method based on rime algorithm (RIME) optimized variational mode decomposition (VMD) and refined composite multi-scale fluctuation dispersion entropy (RCMFDE). Meanwhile, it employs the advantages of Deep Belief Networks (DBN) in nonlinear high-dimensional data processing to classify and recognize the failure modes of the cutting head. Firstly, the paper obtains the optimal parameter combinations of the VMD algorithm through the RIME algorithm. It uses the optimized VMD to adaptively decompose the cutting vibration signal and get a series of intrinsic modal functions (IMF). The paper combined the correlation coefficients to screen the optimal eigencomponent. Then, for the feature IMF component, this study explores the impact of the embedding dimension and category number of RCMFDE on the feature extraction performance. It calculates the RCMFDE of vibration signals from different cutting heads and uses them as the eigenvector. Finally, the paper uses the DBN model to train and test the cutting vibration features and realize the fault pattern recognition of the cutting head. The simulation and experimental results show that the proposed method can effectively extract the fault characteristics of cutting vibration signals, and the recognition accuracy reaches 99.164%. Compared with other methods, it has better recognition accuracy and robustness and can provide a new research idea for monitoring the health status of the cutting head.

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Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. Jin, X., Zhao, G., Zhao, L., Lin, G.: Computer-aided efficient design and performance optimization of cutting head for roadheader. Sci. Rep. 12(1), 6788 (2022). https://doi.org/10.1038/s41598-022-10702-1

    Article  MATH  Google Scholar 

  2. Xin, M., Ye, Z., Zhao, Y., Liu, X., Liu, L., Ge, H., et al.: Comprehensive analysis of aero-engine vibration signals based on wavelet transform method. EURASIP J. Adv. Signal Process. (2023). https://doi.org/10.1186/s13634-023-01079-y

    Article  MATH  Google Scholar 

  3. Barai, V., Dhanalkotwar, V., Ramteke, S.M., Jaju, S.B., Untawale, S., Sharma, A., et al.: Intelligent fault diagnosis of scuffed piston rings using vibration signature analysis. J. Vibr. Eng. Technol. (2023). https://doi.org/10.1007/s42417-023-00891-y

    Article  Google Scholar 

  4. Huang, D., Li, S., Qin, N., Zhang, Y.: Fault diagnosis of high-speed train bogie based on the improved-CEEMDAN and 1-D CNN algorithms. IEEE Trans. Instrum. Meas. 70, 1–11 (2021). https://doi.org/10.1109/tim.2020.3047922

    Article  MATH  Google Scholar 

  5. Yang, Y., Liu, H., Han, L., Gao, P.: A feature extraction method using VMD and improved envelope spectrum entropy for rolling bearing fault diagnosis. IEEE Sens. J. 23(4), 3848–3858 (2023). https://doi.org/10.1109/jsen.2022.3232707

    Article  MATH  Google Scholar 

  6. Liu, F., Wang, H., Li, W., Zhang, F., Zhang, L., Jiang, M., et al.: Fault diagnosis of rolling bearing combining improved AWSGMD-CP and ACO-ELM model. Measurement (2023). https://doi.org/10.1016/j.measurement.2023.112531

    Article  MATH  Google Scholar 

  7. Luo, J., Wen, G., Lei, Z., Su, Y., Chen, X.: Weak signal enhancement for rolling bearing fault diagnosis based on adaptive optimized VMD and SR under strong noise background. Meas. Sci. Technol. (2023). https://doi.org/10.1088/1361-6501/acb83d

    Article  MATH  Google Scholar 

  8. Li, D., Li, M., Yang, L., Wang, X., Zhang, F., Liang, Y.: Rolling bearing fault diagnosis in strong noise background based on vibration signals. SIViP 18(2), 1295–1303 (2023). https://doi.org/10.1007/s11760-023-02846-y

    Article  MATH  Google Scholar 

  9. Zhang, D., Zhang, C., Han, X., Wang, C.: Improved DBO-VMD and optimized DBN-ELM based fault diagnosis for control valve. Meas. Sci. Technol. 35(7), 1–15 (2024). https://doi.org/10.1088/1361-6501/ad3be0

    Article  MATH  Google Scholar 

  10. Wang, Y., Markert, R., Xiang, J., Zheng, W.: Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system. MSSP. 60–61, 243–251 (2015). https://doi.org/10.1016/j.ymssp.2015.02.020

    Article  MATH  Google Scholar 

  11. Zhang, Q., Chen, S., Fan, Z.P.: Bearing fault diagnosis based on improved particle swarm optimized VMD and SVM models. Adv. Mech. Eng. (2021). https://doi.org/10.1177/16878140211028451

    Article  MATH  Google Scholar 

  12. Liu, Z., Peng, Y.: Study on denoising method of vibration signal induced by tunnel portal blasting based on WOA-VMD algorithm. Appl. Sci. (2023). https://doi.org/10.3390/app13053322

    Article  MATH  Google Scholar 

  13. Su, H., Zhao, D., Heidari, A.A., Liu, L., Zhang, X., Mafarja, M., et al.: RIME: a physics-based optimization. Neurocomputing 532, 183–214 (2023). https://doi.org/10.1016/j.neucom.2023.02.010

    Article  MATH  Google Scholar 

  14. Zheng, J., Pan, H., Tong, J., Liu, Q.: Generalized refined composite multiscale fuzzy entropy and multi-cluster feature selection based intelligent fault diagnosis of rolling bearing. ISAT. 123, 136–151 (2022). https://doi.org/10.1016/j.isatra.2021.05.042

    Article  MATH  Google Scholar 

  15. Xu, L., Li, W., Zhang, B., Zhu, Y., Lang, C.: Fault diagnosis of mine truck hub drive system based on LMD multi-component sample entropy fusion and LS-SVM. Actuators. (2023). https://doi.org/10.3390/act12120468

    Article  Google Scholar 

  16. Xiao, M., Wang, Z., Zhao, Y., Geng, G., Dustdar, S., Donta, P.K., et al.: A new fault feature extraction method of rolling bearings based on the improved self-selection ICEEMDAN-permutation entropy. ISAT. 143, 536–547 (2023). https://doi.org/10.1016/j.isatra.2023.09.009

    Article  MATH  Google Scholar 

  17. He, D., Zhang, Z., Jin, Z., Zhang, F., Yi, C., Liao, S.: RTSMFFDE-HKRR: a fault diagnosis method for train bearing in noise environment. Measurement 239, 1–22 (2025). https://doi.org/10.1016/j.measurement.2024.115417

    Article  MATH  Google Scholar 

  18. Rostaghi, M., Khatibi, M.M., Ashory, M.R., Azami, H.: Fuzzy dispersion entropy: a nonlinear measure for signal analysis. IEEE Trans. Fuzzy Syst. 30(9), 3785–3796 (2022). https://doi.org/10.1109/tfuzz.2021.3128957

    Article  MATH  Google Scholar 

  19. Zhenya, Q., Xueliang, Z.: Rolling bearing fault diagnosis based on CS-optimized multiscale dispersion entropy and ML-KNN. J. Brazil. Soc. Mech. Sci. Eng. (2022). https://doi.org/10.1007/s40430-022-03643-3

    Article  MATH  Google Scholar 

  20. Alsalaet, J.: Detection of transients in vibration signals using reverse dispersion entropy. J. Vibr. Acoust. (2022). https://doi.org/10.1115/1.4052749

    Article  Google Scholar 

  21. Zhou, N., Wang, L.: Triple feature extraction method based on multi-scale dispersion entropy and multi-scale permutation entropy in sound-based fault diagnosis. FrPhy. (2023). https://doi.org/10.3389/fphy.2023.1180595

    Article  MATH  Google Scholar 

  22. Li, Y.X., Jiao, S.B., Geng, B., Zhang, Q., Zhang, Y.M.: A comparative study of four nonlinear dynamic methods and their applications in classification of ship-radiated noise. Defence Technol. 18(2), 183–193 (2022). https://doi.org/10.1016/j.dt.2020.11.011

    Article  MATH  Google Scholar 

  23. Yang, J., Bai, Y., Cheng, Y., Cheng, R., Zhang, W., Zhang, G.: A new model for bearing fault diagnosis based on optimized variational mode decomposition correlation coefficient weight threshold denoising and entropy feature fusion. Nonlinear Dyn. 111(18), 17337–17367 (2023). https://doi.org/10.1007/s11071-023-08728-9

    Article  MATH  Google Scholar 

  24. . !!! INVALID CITATION !!! [22, 23].

  25. Zhang, P., Chen, X., Qing, L.: Internal leakage diagnosis of a hydraulic cylinder based on optimization DBN using the CEEMDAN technique. SV. 2021, 1–10 (2021). https://doi.org/10.1155/2021/8856835

    Article  MATH  Google Scholar 

  26. Gao, S., Xu, L., Zhang, Y., Pei, Z.: Rolling bearing fault diagnosis based on SSA optimized self-adaptive DBN. ISAT. 128, 485–502 (2022). https://doi.org/10.1016/j.isatra.2021.11.024

    Article  MATH  Google Scholar 

  27. Jin, Z., He, D., Wei, Z.: Intelligent fault diagnosis of train axle box bearing based on parameter optimization VMD and improved DBN. Eng. Appl. Artif. Intell. (2022). https://doi.org/10.1016/j.engappai.2022.104713

    Article  MATH  Google Scholar 

  28. Zhu, W., Fang, L., Ye, X., Medani, M., Escorcia-Gutierrez, J.: IDRM: Brain tumor image segmentation with boosted RIME optimization. Comput. Biol. Med. 166, 107551 (2023). https://doi.org/10.1016/j.compbiomed.2023.107551

    Article  Google Scholar 

  29. . !!! INVALID CITATION !!! [28, 29].

  30. Liu, X., Shu, L.: Fault detection of rotating machinery based on marine predator algorithm optimized resonance-based sparse signal decomposition and refined composite multiscale fluctuation dispersion entropy. Rev. Sci. Instrum. (2022). https://doi.org/10.1063/5.0096613

    Article  MATH  Google Scholar 

  31. Li, C., Peng, T., Zhu, Y.: A novel approach for acoustic signal processing of a drum shearer based on improved variational mode decomposition and cluster analysis. Sensors (Basel). 20(10), 2949 (2020). https://doi.org/10.3390/s20102949

    Article  MATH  Google Scholar 

  32. Peng, T., Li, C., Zhu, Y.: Design and application of simulating cutting experiment system for drum shearer. Appl. Sci. (2021). https://doi.org/10.3390/app11135917

    Article  MATH  Google Scholar 

  33. Yang, H., Ll, Li., Li, G.H., Guan, Q.R.: A novel feature extraction method for ship-radiated noise. Defence Technol. 18(4), 604–617 (2022). https://doi.org/10.1016/j.dt.2021.03.012

    Article  MATH  Google Scholar 

  34. Hu, Y., Wang, X., Wang, S., Liu, W.: Coating damage detection of vessels using machine learning-based underwater electric field. IEEE Sens. J. 23(7), 7956–7967 (2023). https://doi.org/10.1109/jsen.2023.3243280

    Article  MATH  Google Scholar 

  35. Wang, F., Ma, M., Fu, R., Zhang, X.: EEG-based detection of driving fatigue using a novel electrode. Sens. Actuat. A: Phys. (2024). https://doi.org/10.1016/j.sna.2023.114895

    Article  MATH  Google Scholar 

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Acknowledgements

The present work is supported by the Natural Science Research Project of Anhui Educational Committee (2023AH051196), Open Fund of China State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines (No.SKLMRDPC22KF26), Open Fund of Anhui Intelligent Mining Technology and Equipment Engineering Research Center (No.AIMTEEL202202), Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology (2022yjrc63), The University Synergy Innovation Program of Anhui Province (No.GXXT-2022-019).

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Authors and Affiliations

  1. Anhui Intelligent Mining Technology and Equipment Engineering Research Center, Huainan, 232001, China

    Changpeng Li

  2. State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan, 232001, China

    Changpeng Li, Tianbing Ma, Rui Shi, Qicheng Yang & Ting Yang

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  1. Changpeng Li
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Contributions

CL is responsible for the entire experiment execution and paper writing. TM is responsible for the review and correction of papers. RS and QY are responsible for guiding the experiment. TY is responsible for checking and reviewing manuscripts. This study is funded by CL and TM.

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Correspondence to Tianbing Ma.

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Li, C., Ma, T., Shi, R. et al. A fault identification method for cutting head of the roadheader based on parameter optimization VMD and RCMFDE. SIViP 19, 319 (2025). https://doi.org/10.1007/s11760-025-03884-4

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