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Hydraulic Rock Drill Fault Classification Using X−Vectors

Author

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  • Huixuan Ling

    (School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
    iFLYTEK Research, iFLYTEK Co., Ltd., Hefei 230088, China)

  • Tian Gao

    (iFLYTEK Research, iFLYTEK Co., Ltd., Hefei 230088, China)

  • Tao Gong

    (iFLYTEK Research, iFLYTEK Co., Ltd., Hefei 230088, China)

  • Jiangzhao Wu

    (iFLYTEK Research, iFLYTEK Co., Ltd., Hefei 230088, China)

  • Liang Zou

    (School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

Hydraulic rock drills are widely used in drilling, mining, construction, and engineering applications. They typically operate in harsh environments with high humidity, large temperature differences, and vibration. Under the influence of environmental noise and operational patterns, the distributions of data collected by sensors for different operators and equipment differ significantly, which leads to difficulty in fault classification for hydraulic rock drills. Therefore, an intelligent and robust fault classification method is highly desired. In this paper, we propose a fault classification technique for hydraulic rock drills based on deep learning. First, considering the strong robustness of x−vectors to the features extracted from the time series, we employ an end−to−end fault classification model based on x−vectors to realize the joint optimization of feature extraction and classification. Second, the overlapping data clipping method is applied during the training process, which further improves the robustness of our model. Finally, the focal loss is used to focus on difficult samples, which improves their classification accuracy. The proposed method obtains an accuracy of 99.92%, demonstrating its potential for hydraulic rock drill fault classification.

Suggested Citation

  • Huixuan Ling & Tian Gao & Tao Gong & Jiangzhao Wu & Liang Zou, 2023. "Hydraulic Rock Drill Fault Classification Using X−Vectors," Mathematics, MDPI, vol. 11(7), pages 1-14, April.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1724-:d:1115744
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    References listed on IDEAS

    as
    1. Bo Zhang & Caicai Zhou & Wei Li & Shengfei Ji & Hengrui Li & Zhe Tong & See-Kiong Ng, 2022. "Intelligent Bearing Fault Diagnosis Based on Open Set Convolutional Neural Network," Mathematics, MDPI, vol. 10(21), pages 1-22, October.
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