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

Juanli Li; Jiacheng Xie; Zhaojian Yang; Junjie Li

doi:10.3390/s18061920

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

Sensors (Basel). 2018 Jun 13;18(6):1920. doi: 10.3390/s18061920.

Authors

Juanli Li^{1

2}, Jiacheng Xie³, Zhaojian Yang⁴, Junjie Li⁵

Affiliations

¹ Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China. lijuanli@tyut.edu.cn.
² Post-Doctoral Scientific Research Station, Shanxi Coking Coal Group Co., Ltd., Taiyuan 030024, China. lijuanli@tyut.edu.cn.
³ Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China. xiejiacheng0007@link.tyut.edu.cn.
⁴ Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China. yangzhaojian@tyut.edu.cn.
⁵ Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China. lijunjie0051@link.tyut.edu.cn.

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.

Keywords: Dezert-Smarandache Theory (DSmT); Internet of Things (IoT); ZigBee; fault diagnosis; mine hoist.