RAILWAY TECHNOLOGY 07
Predictive maintenance: Safety and cost-effectiveness enhancement in sustainable railway services Dr.-Ing Ekkarut Viyanit In the new era of mobility and logistics, a rail system will play a crucial role in land transportation of passengers and goods due to its more superior safety, cost competitiveness and environmental friendliness compared to other transportation modes. According to Thailand’s vision to become a regional railway network hub in South-East Asia by 2027, it is very important to prepare the existing and upcoming railway systems with sufficient quality in comparison with neighbouring countries. It is widely agreed that a modernised maintenance management system is one of the most effective and efficient tools for ensuring the reliability, availability, maintainability and safety of railway services in all perspectives. Due to the large role played by maintenance in the life-cycle cost of railway assets, the National Science and Technology Development Agency (NSTDA), Thailand is conducting research and development in the field of predictive maintenance corresponding to technical challenges in the railway business nationwide. It is believed that an efficient and effective maintenance strategy can improve the quality of railway services at an optimal cost throughout a railway life cycle.
CHALLENGES AND BENEFITS OF PREDICTIVE MAINTENANCE IN RAILWAY APPLICATIONS A conventional approach by maintenance in railway assets, e.g., rolling stock, infrastructure, signaling system etc., has mainly relied on periodical maintenance, i.e., time-based or mileage-based maintenance. Based on experiences of many railway operators worldwide,
it is realised that periodical maintenance should be replaced by further developed maintenance technology in order to address current constraints in dealing with improved ecosystem sustainability and cost competitiveness. Due to technological knowledge enhancement, together with developing advanced devices and systems for monitoring and diagnosis, predictive maintenance has been developed and sub-categorized under condition-based maintenance (CBM) as defined by EN13306 (CEN, 2001). In fact, predictive maintenance is classified as a means of data-driven decisions based on algorithms and carried out at an appropriate time prior to the occurrence of a failure event as shown in Figure 1. Currently, it is very challenging for a technical crew to execute the just-in-time maintenance shortly ahead of the failure event. Therefore,
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