Laudehr recently authored a peer-reviewed paper on the Automated Roller Monitoring System – ARMS – examining how distributed acoustic sensing, or DAS, adaptive algorithms and machine learning can improve conveyor condition monitoring in mining environments. While DAS technology has attracted growing interest in conveyor monitoring, Laudehr said turning raw acoustic data into deployable maintenance information requires more than sensing alone. “Every conveyor system operates under different conditions, and ARMS enables this variability to be accounted for through site-specific tailoring. Developed by REMA TIP TOP and Terra15, ARMS combines Terra15’s DAS technology with REMA TIP TOP’s conveyor maintenance expertise to provide continuous monitoring of conveyor rollers. Laudehr said balancing system sensitivity with usability is essential as conveyor monitoring expands across larger mining operations.