Rail infrastructure owners have clear guidelines for the speeds and weights that wagons and carriages should adhere to when using the network. Excessive speed and over-loading of rolling stock is responsible for excessive wear and tear on rail networks. At present, the cost of maintenance due to these factors is incurred by infrastructure operators, as there is no way of knowing which train operating company (TOC) is responsible for going too fast or overloading their wagons. As the SW3 washers are geo-tagged to a precise position, and all measurements are time-stamped, combining data from the SW Ecosystem with data regarding line usage will be able to pinpoint exactly when TOC’s make infractions that cause excessive wear, and will enable rail infrastructure operators to seek remedial costs from the offending TOC’s.
The Smart Washer Ecosystem offers a full ‘end-to-end’ solution, that combines data collection, data integration, and communication as a full package. As the SW itself is embedded within the critical fastening, the SW Ecosystem offers a paradigm shift by embedding monitoring technology within the asset of interest and connecting this to the Internet of Things, giving measurements from within the asset, rather than measuring proxy measure outputs of the asset to give an indication of function (such as current form the drive motor, seen in competitor products). The competitor solutions for monitoring switches only offer data collection functionality. Moreover, the DCU reports the exact GPS co-ordinates of each SW installed; this means that maintenance teams will be able to precisely locate any fault, and thus prevent incorrect inspection of faults, such as that happened before the Potters Bar derailment.
SW3 has a number of cost-saving benefits associated with its use. Switches/points are manually inspected every 6-13 weeks; the annual maintenance cost is 5-15% of the original installation price (40,000 to 150,000 EUR per switch8, whilst the overall lifetime costs for maintaining a point are between 2-3x the original asset price. The annual maintenance bill incurred by Network Rail (NR) for switches is ~£208 million. As a cause of wider signalling failure, points failure were a contributor to compensation of £167 million paid by NR to train operators in 2014, and costing the UK economy a further £22 million. Improving the RCM of switches/points enables cost-savings by RCM and CBM, as maintenance activities are planned with the maximum interval between repairs, and minimises the number & cost of unscheduled outages created by system failures. The 2010 McNulty report on UK rail indicates savings of £500 million to £1 billion could be saved yearly with better asset and supply chain management. SCT have calculated that if the SW Ecosystem can extend the life of a point by just 5%, up to EUR 80,000 could be saved. This will be of particular use for points found at large rail termini, which can be difficult to maintain due to constant heavy traffic. By helping to prevent derailments, the SW Ecosystem will help to:
i) reduce the 2,213 significant accidents each year on the EU-28 railway network9, which have accumulated costs as high as EUR 1.7 billion.
ii) reduce the >€200 million cost to the EU each year associated with repairs after freight train derailments10
Furthermore, the SW Ecosystem will provide customers with big data for predictive maintenance; McKinsey estimates the potential added value of Big Data in transport to 5-10%. For the rail sector this represents a potential added value of £6 billion to £13 billion.
Non-economic benefits of the SW Ecosystem include: Improving trackside safety for rail workers (SW3 enables faster interrogation of switch fidelity from a distance). This will help to reduce the number of trackside workers killed (92 in 201411) or injured at work, of which 48% are caused by accidents involving rolling stock in motion.
