Periodic Reporting for period 1 - GRAILS-SWE (Greater RAIL Safety using the Smart Washer Ecosystem)
Período documentado: 2017-01-01 hasta 2017-09-30
Reliable rail networks are vital for the movement of people and goods across the EU; 9% of all EU freight (280 billion tonne-kilos) and 381 billion passenger kilometres1 were travelled on the EU-28 rail networks in 2014.
Train derailment is the most commonly investigated type of serious rail accident in Europe. Derailments represent 4% of all significant accidents; in 2012, there were 97 significant derailments. Many more non-significant derailments occur annually; the FP7-funded D-RAIL project estimated 500 open line freight train derailments occur in Europe each year, of which 7% involve dangerous goods. A railroad switch (point) is a mechanical installation enabling trains to be guided from one track to another. Point integrity is vital; there are on average 31 critical fastenings at each switch. Switch component failure (SCF) cause delays at best, and derailments at worse. In the UK, SCF was the second biggest cause of derailment from 2005-2010; across the whole EU, SCF was responsible for 21 derailments between 2005-2010. Globally, SCF is responsible for 5.77% of derailments – making SCF the 6th largest cause of derailments globally. Missing or faulty bolts at points caused the 2002 Potters Bar, 2007 Grayrigg, and 2013 Bretigny-sur-Orge passenger train derailments, all of which incurred fatalities. More widely, SCF is a leading cause of signalling failure, leading to delays; 9.5 million minutes of passenger delays due to SCF occurred during the 2015/2016 audit period on UK rail networks alone.
Following successful trials with London Underground, SCT secured SME instrument Phase 1 study in order to undertake a detailed investigation of routes to exploit SW3 in rail, tram and light rail networks in four key European territories – France, Germany, Spain, and Sweden.
Specific objectives for the feasibility study:
a) EU Regulatory/Certification/Operational Requirements
b) End user / Stakeholder Engagement + Feedback
c) Technical Development & Integration Evaluation
d) Business Models & Exploitation Strategy
e) Supply chain assessment
Train derailment is the most commonly investigated type of serious rail accident in Europe. Derailments represent 4% of all significant accidents; in 2012, there were 97 significant derailments. Many more non-significant derailments occur annually; the FP7-funded D-RAIL project estimated 500 open line freight train derailments occur in Europe each year, of which 7% involve dangerous goods. A railroad switch (point) is a mechanical installation enabling trains to be guided from one track to another. Point integrity is vital; there are on average 31 critical fastenings at each switch. Switch component failure (SCF) cause delays at best, and derailments at worse. In the UK, SCF was the second biggest cause of derailment from 2005-2010; across the whole EU, SCF was responsible for 21 derailments between 2005-2010. Globally, SCF is responsible for 5.77% of derailments – making SCF the 6th largest cause of derailments globally. Missing or faulty bolts at points caused the 2002 Potters Bar, 2007 Grayrigg, and 2013 Bretigny-sur-Orge passenger train derailments, all of which incurred fatalities. More widely, SCF is a leading cause of signalling failure, leading to delays; 9.5 million minutes of passenger delays due to SCF occurred during the 2015/2016 audit period on UK rail networks alone.
Following successful trials with London Underground, SCT secured SME instrument Phase 1 study in order to undertake a detailed investigation of routes to exploit SW3 in rail, tram and light rail networks in four key European territories – France, Germany, Spain, and Sweden.
Specific objectives for the feasibility study:
a) EU Regulatory/Certification/Operational Requirements
b) End user / Stakeholder Engagement + Feedback
c) Technical Development & Integration Evaluation
d) Business Models & Exploitation Strategy
e) Supply chain assessment
The feasibility study undertaken had specific objectives, which can be seen below. Each task of the study has a specific report section as a component or sub-deliverable, in accordance with the work programme.
• EU Regulatory/Certification/Operational Requirements – an investigation of the regulatory and operational landscape in each of the non-UK four initial markets.
• Technical Development & Integration Evaluation – reviewing any system development needs identified through stakeholder engagement, as well as assessing any challenges that may be identified interfacing with any existing systems in place in each of the target EU countries.
• Business Models & Exploitation Strategy. Value proposition development for future purchasers of SMART S&C in each territory. Market assessment and prioritisation. For each end-user type, an analysis of the following: i) Size of market; ii) Barriers to entry for each market; iii) End-user engagement via customer survey to delineate user needs and requirements; iv) Competitor assessment; v) Routes to market, including identification of Tier 1 asset management and systems integrators; vi) Identification of Phase 2 partners. For switch manufacturers, engagement will be centered on how to adopt SMART S&C + DCU Ecosystem into their assets for sale as a complete package. Exploitation: i) Gap analysis of current capabilities; ii) Modelling different sales and licensing scenarios; iii) Commercial risk assessment for each scenario; iv) IP retention options.
• Supply chain assessment – i) Identification of third parties for scale-up activities/manufacture of key components and/or assembly of SMART S&C; ii) Analysis of requirements; required to keep production in-house at scale and quality required for each end-user type.
• EU Regulatory/Certification/Operational Requirements – an investigation of the regulatory and operational landscape in each of the non-UK four initial markets.
• Technical Development & Integration Evaluation – reviewing any system development needs identified through stakeholder engagement, as well as assessing any challenges that may be identified interfacing with any existing systems in place in each of the target EU countries.
• Business Models & Exploitation Strategy. Value proposition development for future purchasers of SMART S&C in each territory. Market assessment and prioritisation. For each end-user type, an analysis of the following: i) Size of market; ii) Barriers to entry for each market; iii) End-user engagement via customer survey to delineate user needs and requirements; iv) Competitor assessment; v) Routes to market, including identification of Tier 1 asset management and systems integrators; vi) Identification of Phase 2 partners. For switch manufacturers, engagement will be centered on how to adopt SMART S&C + DCU Ecosystem into their assets for sale as a complete package. Exploitation: i) Gap analysis of current capabilities; ii) Modelling different sales and licensing scenarios; iii) Commercial risk assessment for each scenario; iv) IP retention options.
• Supply chain assessment – i) Identification of third parties for scale-up activities/manufacture of key components and/or assembly of SMART S&C; ii) Analysis of requirements; required to keep production in-house at scale and quality required for each end-user type.
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.
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.