Next Article in Journal
Error Analysis of Magnetohydrodynamic Angular Rate Sensor Combing with Coriolis Effect at Low Frequency
Next Article in Special Issue
Cognitive Interference Alignment Schemes for IoT Oriented Heterogeneous Two-Tier Networks
Previous Article in Journal
An Improved Strong Tracking Cubature Kalman Filter for GPS/INS Integrated Navigation Systems
Previous Article in Special Issue
A Design of Small Area, 0.95 mW, 612–1152 MHz Open Loop Injection-Locked Frequency Multiplier for IoT Sensor Applications
Article Menu
Issue 6 (June) cover image

Export Article

Version is current.

Open AccessArticle
Sensors 2018, 18(6), 1920;

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

Shanxi Key Laboratory of Fully Mechanized Coal Mining Equipment, College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan 030024, China
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
Full-Text   |   PDF [3770 KB, uploaded 13 June 2018]   |  


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)

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top