Predictive Prioritization of Railway Bearings Using Acoustic Similarity of NOISY(RS1) Alarms from Wayside Monitoring Systems

Abstract

The operational reliability of heavy haul railways, such as the Carajás Railway (EFC), depends on early detection of failures in critical components like bearings. This study proposes a predictive prioritization approach based on acoustic similarity analysis of NOISY(RS1) alarms from the RailBAM® wayside monitoring system. Traditionally discarded due to suspected interference, these alarms have shown statistical overlap with confirmed failures. By applying multivariate similarity analysis using Mahalanobis distance and acoustic parameters—ERS DB, ERS Neighbors DB, and ΔERS DB—the methodology identifies patterns indicative of real defects. A new rule was developed to reclassify NOISY(RS1) alarms based on statistical thresholds and repetition criteria, enhancing failure detection accuracy. Experimental validation revealed previously unprioritized bearings with physical damage, demonstrating the rule’s potential to complement existing predictive matrices. The approach improves maintenance planning, reduces undetected failures, and supports the integration of data-driven strategies in Prognostics and Health Management (PHM) for railway assets.