The authors propose a new method of predictive replacements of belt sections in a conveyor belt loop with their refurbishment based on their condition recognized by specially designed diagnostic devices: DiagBelt and BeltSonic. Selection of the moment for belt replacement is made on the basis of forecasted state of its wear, when steel cable failure density exceeds the selected threshold or the percentage of belt covers abrasion exceeds the permissible level. The use of double belt refurbishment extends the total belt operation time, but without diagnostics, the reconditioning success rate is relatively low. Visual inspection allows only an uncertain assessment of cable condition. New devices give precise assessment of their condition and allow the selection of a non-linear rate of belt condition based on consecutive scans. The assessed condition of the cables is the key factor in successful belt refurbishment and obtained cost savings. Additionally, diagnostics can help extend belt life. Identified failures can be repaired in order to prevent accelerated belt degradation. The use of diagnostics in the current replacement strategy significantly improves the economic efficiency of belt reconditioning. The reuse of the belt core ensures efficient use of resources and reduces the level of waste, which fits into the circular economy.

Non-invasive conveyor belt diagnostics in damage detection allows significant reductions of the costs related to belt replacement, as well as the evaluation of belt usability and wear degree changes over time. As a result, it increases safety in the location where the belt is used. Depending on the location of a belt conveyor, its length or the type of the transported material, the belt may undergo wear at different rates, albeit the wear process itself is inevitable. This article presents an artificial intelligence-based approach to the classification of conveyor belt damage. A two-layer neural network was implemented in the MATLAB programming language, with the use of a Deep Learning Toolbox set. As a result of the optimization of the created network, the effectiveness of operation was at the level of 80%.

Diagnostics of conveyor belts used in horizontal transport without the need to take the belt off the conveyor and test it in laboratory conditions is an important aspect in mining plants (Jurdziak et al., Adv Intell Syst Comput, 835:645–654, 2019). Current testing, and thus obtaining knowledge about the current thickness of the conveyor belt covers, allows for control accelerated changes. It also avoids emergency stoppages in the operation of the conveyor and enables economically justified planning of a break in its operation. The article presents the concept of the first in Poland mobile device for measuring the thickness of conveyor belts in motion, implemented as part of the NCBR project (No. 0227 / L-10/2018 [LIDER program, Transport Przemysłowy i Maszyny Robocze, 1(47)/2020, pp. 60–61]), and also presents the measurement results obtained thanks to the laboratory version of the device.