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Abnormal relative position and attitude offset may cause powered supports to collide during movement, affecting the stability of a longwall operation. This study proposed a collision detection method for virtual powered support groups in the undulating strata condition. First, a support workspace was developed by defining key points, and the Denavit–Hartenberg (DH) matrix coordinate system of powered supports was established to solve the key points. Then, based on the intersection rule of plane line segments, collision detection between powered supports was realized by calculating the coordinates of the key points. According to actual coal face data, a virtual simulation platform is established, and the sequential movement of support groups on the virtual floor was realized. Finally, contrast experiments of different virtual floor models were conducted, and the Mesh floor, which is closer to the actual coal seam, was selected for subsequent virtual simulation. The collision detection experiments of powered supports were conducted in both virtual and physical environments, and the results validated the feasibility of the collision detection method. This method improves the virtual simulation degree of longwall coal mining, and provides simulation reference for the attitude adjustment of actual powered support groups before moving.

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.