Hydraulic Rock Drill Fault Classification Using X−Vectors

Huixuan Ling, Tian Gao, Tao Gong, Jiangzhao Wu and Liang Zou ()
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Huixuan Ling: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, 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

Mathematics, 2023, vol. 11, issue 7, 1-14

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.

Keywords: x?vectors; hydraulic rock drill; PHM; focal loss (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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