Abstract
Belt conveyors are commonly used for the transportation of bulk materials. The most characteristic design feature is the fact that thousands of idlers are supporting the moving belt. One of the critical elements of the idler is the rolling element bearing, which requires monitoring and diagnostics to prevent potential failure. Due to the number of idlers to be monitored, the size of the conveyor, and the risk of accident when dealing with rotating elements and moving belts, monitoring of all idlers (i.e., using vibration sensors) is impractical regarding scale and connectivity. Hence, an inspection robot is proposed to capture acoustic signals instead of vibrations commonly used in condition monitoring. Then, signal processing techniques are used for signal pre-processing and analysis to check the condition of the idler. It has been found that even if the damage signature is identifiable in the captured signal, it is hard to automatically detect the fault in some cases due to sound disturbances caused by contact of the belt joint and idler coating. Classical techniques based on impulsiveness may fail in such a case, moreover, they indicate damage even if idlers are in good condition. The application of the inspection robot can “replace” the classical measurement done by maintenance staff, which can improve the safety during the inspection. In this paper, the authors show that damage detection in bearings installed in belt conveyor idlers using acoustic signals is possible, even in the presence of a significant amount of background noise. Influence of the sound disturbance due to the belt joint can be minimized by appropriate signal processing methods.
Highlights
Belt conveyors are widely recognized as interesting objects for condition monitoring [1]
We propose a combination of robotics inspection, acoustic data measurement, and signal processing for fault detection in idlers
To perform the analysis of cyclic behavior in the presence of large impacts originating from the mechanical joint passing over idlers, a spectral autocorrelation map has been calculated with the range parameter K = 1 s
Summary
Belt conveyors are widely recognized as interesting objects for condition monitoring [1]. There are other interesting research problems regarding conveyors, including destructive testing of steel-core belts, modeling of material stream behavior in a transfer point (between two conveyors), or detection of humans in harsh conditions (for the case when they are using conveyors as transport means for miners) [19,20,21]. These topics have no direct link to the predictive maintenance of such systems
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