It is well known that mechanical systems require supervision and maintenance procedures. There are a lot of condition monitoring techniques that are commonly used, and in the era of IoT and predictive maintenance one may find plenty of solutions for various applications. Unfortunately in the case of belt conveyors used in underground mining a list of possible solutions shrinks quickly. The reason is that they are specific mechanical systems—the typical conveyor is located in the mining tunnel and its length may vary between 100 and 1000 m. According to mining regulations, visual inspection of the conveyor route should be done before it will start the operation. On the other hand, since environmental conditions in mining tunnels are extremely harsh and the risk of accidents is high, there is a tendency to minimize human presence in the tunnels. In this paper, we propose a prototype of an inspection robot based on a UGV platform that could support maintenance staff during the inspection. At present, the robot is controlled by an operator using radio however, we plan to make it autonomous. Moreover, its support could be significant—the robot can “see” elements of the conveyor route (RGB camera) and can identify hot spots using infrared thermography. Moreover, the detected hot spots could be localized and its position can be stored together with both types of images. In parallel, it is possible to preview images in a real-time and stored data allow analysing state of conveyor system after the inspection mission. It is also important that due to radio control systems, an operator can stay in a safe place. Such a robot can be classified as a mobile monitoring system for spatially distributed underground infrastructure.

Usually, substantial part of a mine haulage system is based on belt conveyors. Reliability of such system is significant in terms of mining operation continuity and profitability. Numerous methods for conveyor belt monitoring have been developed, although many of them require physical presence of the monitoring staff in the dangerous environment. In this paper, a remote sensing method for assessing a conveyor belt condition using the Terrestrial Laser Scanner (TLS) system has been described. For this purpose a methodology of semi-automatic processing of point cloud data for obtaining the belt geometry has been developed. The sample data has been collected in a test laboratory and processed with the proposed algorithms. Damaged belt surface areas have been successfully identified and edge defects were investigated. The proposed non-destructive testing methodology has been found to be suitable for monitoring the general condition of the conveyor belt and could be exceptionally successful and cost-effective if combined with an unmanned, robotic inspection system.

Damage detection in complex mechanical structures is important for cost-effective and safe operation. Conveyor belts with steel cords are used for bulk material transport in mining companies. Due to harsh environmental conditions, both covers and cords are subjected to damage. As lengths of conveyors may vary from dozens of meters to kilometers, a belt loop consists of many connected belt pieces. Thus, the condition of splices between belt pieces is also critical. For both steel cord damage/wear detection and splice condition evaluations the NDT techniques based on magnetic field measurement and variability analysis are used. To obtain appropriate resolution, multi-channel data are collected. Here we propose a pre-processing technique developed for signal synchronization for biased splices data. The biased splices mean a phase shift between signals from a multi-channel sensor due to the design technology of the splice. As the quality of the splice is related to the appropriate precision of splice production, splice evaluation is defined as a similarity analysis of each signal with respect to the estimated pattern. Due to the mentioned phase shift, signals should be "synchronized" first, before final analysis. In industrial conditions, many factors may influence the signal shape. Thus, the problem of automated synchronization by shifting the signals may be defined as a multidimensional optimization problem. Here, we proposed to use a genetic algorithm with an algorithmically simple cost function for that purpose. In this paper, the authors propose an automated procedure applied to real measurement data and final results. A multidimensional optimization has been compared to simple signal shifting according to several criteria, and GA-based results were the best.

Belt conveyors are one of the most popular methods of material transport in many branches of industry, especially in mining. The average length of mining belt conveyor is about 1000 m. Taking into account that total length of transportation ways in averaged mine can approach several dozen of kilometers and network of several conveyors may cover large area, maintenance of such specific transportation system is very difficult. In this work we propose an automatic multi-channel system for data acquisition and processing for damage detection in belts. Belts with steel cords are considered here, they consist of top and bottom rubber covers and steel cords in between them. Due to many reasons (mainly sharpness of transported materials) covers may be damaged and it may initiate degradation process or straight damages in steel cords. Properties of steel cords are therefore crucial for overall strength of belts, if they are damaged, it may cause catastrophic failure of the whole conveyor. So, monitoring of belts conditions is a crucial issue. We proposed a monitoring system that measure and process data from array of magnetic sensors. The system allows to acquire up to 24 channels of NDT signals and uses automatic algorithms to process them in order to get information about begin of the belt loop, location of joints of particular belt segments and the final location and size of damages related to corrosion or cuts of steel cords inside belts. These techniques will be presented in the paper. Our approach has been validated in a lignite mine for several conveyor belts.