Chusen Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Chusen Wang has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Xiankang Huang
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
2 shared publications - Zuzhi Tian
School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou, China
2 shared publications - Fangwei Xie
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
1 shared publication - Jinjie Ji
School of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, China
1 shared publication - Qian Wang
School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, China
1 shared publication - Xiangfan Wu
School of Mechanical and Electrical Engineering, Xuzhou University of Technology, Xuzhou 221018, China
1 shared publication
Latest Publications
- 28 June 2024
- Published by Springer Nature in Fire Technology
- Vol. 60 (6) , 3829-3851
Research on Mine Smoke Detection Technology Based on Multi-Feature Fusion Analysis
- 28 June 2024
- Published by Springer Nature in Fire Technology
- Vol. 60 (6) , 3829-3851
Research on Mine Smoke Detection Technology Based on Multi-Feature Fusion Analysis
- Open Access
Research on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision
Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.
Open AccessResearch on Belt Deviation Fault Detection Technology of Belt Conveyors Based on Machine Vision
Traditional belt deflection detection devices for underground belt conveyors in coal mines have problems, such as their single function, poor fault location and analysis accuracy, low automation level, and low reliability. In order to solve the defects of traditional detection devices, the belt deviation faults of the underground belt conveyor transport process require to be detected effectively and reliably. This paper proposes a belt deviation detection method based on machine vision. This method makes use of a global adaptive high dynamic range imaging method to complete the brightness enhancement processing of the underground image. Then the straight-line features of the conveyor belt edges are extracted using Canny edge detection and the Hough transform algorithm. In addition, a dual-baseline localization judgment method is proposed to realize the identification of band bias faults. Finally, a test bench for belt conveyor deviation was built. Testing experiments for different deviations were conducted. The accuracy of the tape deviation detection reached 99.45%. The method proposed in this study improves the reliability of belt deviation fault detection of underground belt conveyors in coal mines and has wide application prospects in the field of coal mining.