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

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.

Performance Analysis of IoT-Based Sensor, Big Data Processing, and Machine Learning Model for Real-Time Monitoring System in Automotive Manufacturing

“…The study revealed that knowledge of environmental conditions could help prevent errors during the design phase in additive manufacturing. Li et al utilized IoT sensors to collect data for the fault diagnosis of mine hoisting equipment [ 13 ]. The study revealed that IoT sensors can help provide complete diagnosis data as well as improve diagnosis results.…”

“…Recent technologies such as Internet of Things (IoT)-based sensors can be utilized and integrated with monitoring systems. Studies have been conducted in the manufacturing industry and showed significant benefits from the use of IoT-based sensors for monitoring such as working condition improvements [ 11 ], error design prevention [ 12 ], fault diagnosis [ 13 ], quality prediction [ 14 ], and helping managers with better decision making [ 15 ].…”

Improving Intelligence and Efficiency of Salt Lake Production by Applying a Decision Support System Based on IOT for Brine Pump Management

“…In the implementation of the access network part, the GPRS module of the GPRS232-7S2 model is used as the gateway of the LoRa network, which is communicated with the microcontroller through the UART (universal asynchronous receiver/transmitter) port [36,37]. A power distribution room is responsible for the power supply of three adjacent brine pumps, and from the middle of the brine pump closer.…”

A hierarchical fuzzy comprehensive evaluation algorithm for running states of an electromechanical system

“…In recent years, in order to monitor the states of the electrical drive system of the hoist in real time and provide timely maintenance advice to avoid unnecessary disasters and losses, scholars have studied the fault diagnosis of the hoist, mainly researching the diagnosis technologies of the hoist and its corresponding critical equipment. Using the Internet of Things to diagnose the fault of the hoist and basing on the improved Dezert-Smarandache theory, fault diagnosis reasoning can be performed and remote data transmission and fault diagnosis can be achieved (Li, Xie, et al, 2018). Based on the proposed new MMC modular multilevel converter, the change of the measuring point voltage was obtained by changing the output terminal voltage, and the open and short circuit faults were detected (Haghnazari et al, 2016).…”