Advances in railway condition monitoring and fault diagnosis: Transition from industry 4.0 automation to industry 5.0 human-centric maintenance

Failures in wheelsets and bearings are a leading cause of train derailments and reduced system availability. Condition monitoring allows detection of developing faults and estimation of the remaining useful life of components. Recent advances in computer-based fault detection have improved the diagnosis of railway assets such as wheelsets, axles, bearings, and tracks. Industry 4.0 technologies, including big data analytics, cybersecurity, and the Internet of Things, have enhanced detection accuracy, efficiency, and real-time decision-making. However, since Industry 4.0 is so focused on technology, it often ignores things that are important to people. The new idea of Industry 5.0 combines human knowledge with intelligent systems to make maintenance plans that are long-lasting and flexible. This study evaluates the efficacy of railway component defect detection and diagnostic methodologies within the framework of Industry 4.0, and it delves into the possibilities and threats associated with the shift to Industry 5.0 for future railway maintenance systems.