Roos, Willem AbrahamHeyns, P.S. (Philippus Stephanus)2021-06-282021Roos, W.A. & Heyns, P.S. 2021, 'In-belt vibration monitoring of conveyor belt idler bearings by using wavelet package decomposition and artificial intelligence', International Journal of Mining and Mineral Engineering, Vol. 12, No. 1, pp. 48-66.1754-890X (print)1754-8918 (online)10.1504/IJMME.2021.114914http://hdl.handle.net/2263/80631Visual and acoustic methods are commonly used to identify faulty or failing idler bearings but these methods can become tedious and time consuming in practice. While vibration monitoring might look like an obvious choice to explore, the instrumentation of individual idler bearings would be prohibitively expensive. The potential for using an accelerometer that moves with the belt while tracking the condition of all bearings encountered along the way is therefore potentially interesting. This possibility is explored in this work on a laboratory scale test rig. Wavelet package decomposition is used to extract the bearing features and present it to an artificial neural network and support vector machine to identify and classify faulty idler bearings. The system could not only identify faulty bearings but also classify the faults accurately.en© 2021 Inderscience Enterprises Ltd.Artificial intelligence (AI)Vibration transmissibilityConveyorIdler bearing vibration monitoringCondition-based monitoringIn-belt vibration monitoringEngineering, built environment and information technology articles SDG-04SDG-04: Quality educationEngineering, built environment and information technology articles SDG-08SDG-08: Decent work and economic growthEngineering, built environment and information technology articles SDG-09SDG-09: Industry, innovation and infrastructureEngineering, built environment and information technology articles SDG-12SDG-12: Responsible consumption and productionPostprint Article