In this study, a real-time remote monitoring and fault diagnosis method has been developed based on the Internet of Things (IoT) frame perception, and successfully applied to a mine hoist system. The proposed method combines the sensor technology, online monitoring technology, wireless transmission technology, and fault diagnosis technology. The basic structure of the traditional IoT comprises a perception layer, a network layer, and an application layer, the proposed structure contains an additional middleware layer between the network layer and the application layer. This four-layer system is used in a mine hoist remote monitoring and fault diagnosis framework to process heterogeneous multi-source information. The sensors and parameters are connected in the perception layer, the characteristic parameters are obtained using the configuration software, and the mine local area network is saved to the data server, thereby synchronizing real-time data in the local area network. The network layer utilizes mature Internet and long-distance wireless transmission communication technologies, whereas the middleware layer comprises of a Service-Oriented Architecture (SOA)-based IoT data processing framework that integrates the multi-source heterogeneous data. Further, the fault diagnosis method is analyzed and verified based on the gray association rules. In the application layer, a human-computer interface is used for the remote monitoring and diagnosis of the mine hoist and to provide the diagnosis results as feedback to the user. The results using the aforementioned analyses are applied to the remote monitoring and diagnosis of a mine hoist system. In this study, experimental tests are conducted in this study to significantly improve the fault monitoring, diagnostic capabilities, and reliability of the mine hoist system, indicating the good application good prospects of the proposed method.

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.

The performance of the brake system is directly related to the safety and reliability of the mine hoist operation. Mining the useful fault information in the operation of a mine hoist brake system, analyzing the abnormal parts and causes of the equipment, and making accurate early prediction and diagnosis of hidden faults are of great significance to ensure the safe and stable operation of a mine hoist. This study presents a fault diagnosis method for hoist disc brake system based on machine learning. First, the monitoring system collects the information of the hoist brake system, extracts the fault features, and pretreats it by SPSS (Statistical Product and Service Solutions). This work provides data support for fault classification. Then, due to the complex structure of the hoist brake system, the relationship between the fault factors often has a significant impact on the fault. Considering the correlation between the fault samples and the attributes of each sample data, the C4.5 decision tree algorithm is improved by adding Kendall concordance coefficient, and the improved algorithm is used to train the sample data to get the decision tree classification model. Finally, the fault sample of the hoist brake system is trained to get the algorithm model, and then the fault diagnosis rules are generated. The state of the brake system is judged by classifying the data. Experiments show that the improved C4.5 decision tree algorithm takes the relativity of conditional attributes into account, has a higher diagnostic accuracy when processing more data, and has concise and clear fault classification rules, which can meet the needs of hoist fault diagnosis.

We formulated a high birefringence, large dielectric anisotropy, UV stable, and low absorption loss nematic liquid crystal mixture, named UCF-15, for mid-wave infrared (MWIR) applications. To achieve fast response time, we fabricated a polymer network liquid crystal (PNLC) using UCF-15 as host. At 40°C operating temperature, our PNLC shows 2π phase change at λ = 4 μm, submillisecond response time, and over 98% transmittance in the 3.8 to 5.1 μm region. Potential applications of this PNLC phase modulator for high speed laser beam steering, adaptive optics, and optical tweezer are foreseeable.

With regards to the low degree of digitization, lack of real geological terrain, and low degree of automation in the cutting process of the traditional virtual fully mechanized mining face, we studied the key technologies of virtual operation and cutting path planning of the shearer on the three-dimensional (3D) roof and floor based on the virtual reality engine (Unity3D). Firstly, the virtual 3D coal seam was constructed through the 3D geological coordinate data of the mine. On this basis, the shape function of the scraper conveyor with the adaptive configuration on the floor was constructed to obtain the combined operation of the virtual shearer and the scraper conveyor. The movement of the shearer’s walking and height-adjustment was then, analyzed. A strategy for automatic height-adjustment based on the adjustment of the direction of the drum movement is hence, proposed to control the cutting path of the shearer. Finally, different experimental schemes were simulated in the developed prototype system after which each of the schemes was evaluated using the fuzzy comprehensive evaluation method. The results show that the proposed strategy for trajectory control can improve the accuracy and stability of the shearer’s motion trajectory. In Unity3D, the pre-selected schemes and digital and visual planning of coal production are previewed ahead of time, the whole production process can be mapped synchronously in the production process. It is also obtained that the virtual preview and evaluation of the production process can provide some guidance for actual production.