An Industrial IoT Approach to Enable Remote Asset Health Monitoring for Short Line Railroad Operators

This paper presents findings from a comprehensive two-part industrial case study focused on remote asset health monitoring and diagnostics in the railroad industry. The case study was carried out through a multiparty collaboration project between Ridgetop Group Inc. (Ridgetop), Reading and Blue Mountain Northern (RBMN) Railroad, and Innovative Operations Technology LLC in Port Clinton, PA. The first phase of the study, spanning March 2023 to August 2023, involved field testing Ridgetop's original Sentinel Motion Development Kit (SMDK). This kit comprises a wireless network of customized smart sensors that are mounted on Timken bearing end caps and transmit critical vibration and temperature data to a cellular-connected gateway system. The Linux-based gateway hosts Ridgetop's Sentinel MotionView software package for data acquisition, analysis, and sensor-gateway management. Results from this initial phase guided the research team in optimizing the custom sensor hardware design and it led to the development of new software analytics for near real-time monitoring and diagnosis of critical faults and anomalies associated with bearings, wheels, and track conditions. Encouraged by these results, Ridgetop undertook a redesign of the modular SMDK technology elements to align more closely with the IEEE 1856-2017 PHM Standard Framework for Prognostics and Health Management of Electronic Systems. The results from the second phase, spanning September 2023 to April 2024, details Ridgetop's completion of the hardware, firmware, and software redesign process as a means to realize the following eight functional blocks outlined in the IEEE 1856-2017 standard; 1) Sensors (S), 2) Data Acquisition (DA), 3) Data Manipulation (DM), 4) State Detection (SD), 5) Health Assessment (HA), 6) Prognostic Assessment (PA), 7) Advisory Generation (AG), and 8) Health Management (HM).