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Sensors 2018, 18(6), 1920; https://doi.org/10.3390/s18061920

Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment

1
Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2
Post-Doctoral Scientific Research Station, Shanxi Coking Coal Group Co., Ltd., Taiyuan 030024, China
*
Author to whom correspondence should be addressed.
Received: 14 May 2018 / Revised: 8 June 2018 / Accepted: 11 June 2018 / Published: 13 June 2018
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Abstract

To reduce the difficulty of acquiring and transmitting data in mining hoist fault diagnosis systems and to mitigate the low efficiency and unreasonable reasoning process problems, a fault diagnosis method for mine hoisting equipment based on the Internet of Things (IoT) is proposed in this study. The IoT requires three basic architectural layers: a perception layer, network layer, and application layer. In the perception layer, we designed a collaborative acquisition system based on the ZigBee short distance wireless communication technology for key components of the mine hoisting equipment. Real-time data acquisition was achieved, and a network layer was created by using long-distance wireless General Packet Radio Service (GPRS) transmission. The transmission and reception platforms for remote data transmission were able to transmit data in real time. A fault diagnosis reasoning method is proposed based on the improved Dezert-Smarandache Theory (DSmT) evidence theory, and fault diagnosis reasoning is performed. Based on interactive technology, a humanized and visualized fault diagnosis platform is created in the application layer. The method is then verified. A fault diagnosis test of the mine hoisting mechanism shows that the proposed diagnosis method obtains complete diagnostic data, and the diagnosis results have high accuracy and reliability. View Full-Text
Keywords: Internet of Things (IoT); mine hoist; fault diagnosis; ZigBee; Dezert-Smarandache Theory (DSmT) Internet of Things (IoT); mine hoist; fault diagnosis; ZigBee; Dezert-Smarandache Theory (DSmT)
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Li, J.; Xie, J.; Yang, Z.; Li, J. Fault Diagnosis Method for a Mine Hoist in the Internet of Things Environment. Sensors 2018, 18, 1920.

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