A predictive safety and maintenance framework for railway locomotives: integrating HAZOP, FMEA, and IoT-based risk mitigation

Abstract

Safety and maintenance efficiency are critical challenges in the railway industry, particularly in the use of lifting jacks for locomotive maintenance. This study proposes a predictive maintenance framework that integrates the Hazard and Operability Study (HAZOP), Failure Mode and Effects Analysis (FMEA), and Internet of Things (IoT) technology to detect potential failures in real time. A case study was conducted at a locomotive maintenance depot in Indonesia, where several occupational accidents had been recorded due to lifting jack malfunctions. Based on HAZOP and FMEA analyses components such as stoppers and drive motors were identified as having high Risk Priority Numbers (RPN), each reaching 512, indicating significant failure risks. The proposed IoT system employs HCSR-04 and MPU6050 sensors to accurately monitor the height and inclination of the equipment. Evaluation results show that the system effectively detects anomalies with minimal data deviation and a low data loss rate during a 10-day testing period. The implementation of this system significantly reduces workplace accident risks, improves maintenance efficiency, and supports digital transformation within the industrial environment. These findings demonstrate that the integration of HAZOP, FMEA, and IoT is effective for risk mitigation and can be replicated in other railway components. Moreover, this research opens new avenues for developing AI-based predictive systems and implementing digital twins as part of future smart maintenance strategies.