Acoustic-based diagnostics of belt conveyor idlers in real life mining conditions by mobile inspection robot

Abstract

Belt conveyors are commonly used for the transportation of bulk materials, mainly in mining industry. Due to the number of idlers to be monitored, the size of the conveyor, and the risk of accidents when dealing with rotating elements and moving belts, monitoring of all idlers (i.e. using vibration sensors) is impractical regarding scale and connectivity. The application of the inspection robot can "replace" the classical measurement done by maintenance crews. In this paper, a method based on application of mobile inspection robot for capturing acoustic signals instead of commonly used vibrations is proposed. Furthermore, the authors show that damage detection in bearings of idlers using acoustic data is possible, even in the presence of a significant amount of background noise. Influence of the sound disturbance due to the belt joints and other factors can be minimized by appropriate signal processing methods.