CAN THE RAILWAY INDUSTRY UTILIZE DATA TO ITS FULL POTENTIAL?

AuthorsShani Davies, MIEAust C.P.Eng NERJordan Daniels, P.Eng., MBATimmy Li, P.Eng.Rebecca Grill, M.Eng. (WSP Sweden)Jason Baier, EIT

ReviewersJennifer Verellen, P.Eng., IntPEMustafa Mirza, M.A.Sc., P.Eng., PMPTamsin Silvester, ChPP (WSP UK)Chris Walsh, P.Eng.Heather Kruger, MBA, P.Eng., PMP

Matthew Butcher, M.Eng., EITYishu Pu, M.Eng., M.A.Sc., EITHerman Won, P.Eng.Anna Robak, P.Eng., Ph.D.

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The amount of data being created is increasing exponentially, more than doubling every two years. By 2020, 50 billion devices are expected to be connected through the Internet of Things, generating 44 trillion GB of data1. When used correctly and combined with robust data management and analytics, this data can be used to drive maintenance, operation, rehabilitation and upgrade decisions in the railway industry. The result would be a more efficient and reliable railway that provides faster, lower risk, more cost-effective, reliable service for its users.

DATA IN THE RAILWAY

Data has significant value for railway operations. It is estimated that condition monitoring and predictive maintenance can reduce railway maintenance costs by 25% while improving service and increasing asset availability.2 The benefits of data in the railway extend through the life of the project, allowing engineers, operators, and maintainers to make informed decisions, reduce costs, and provide reliable service to users.

In the railway, “big data means big change”3

To fully realize benefits of data for predictive maintenance, abundant high-quality data must be available. This data can then be analyzed to drive maintenance decisions.

To be Future Ready, the railway industry needs to appreciate the value of data for the life of the project, and design for it today.

DATA ACQUISITION

“Technology is rapidly improving to allow for more data to be collected, and the condition of more assets to be monitored in real time.” 4

With data becoming a pre-requisite for technology and sensors becoming cheaper, connected and prolific, it is easier than ever to acquire data from numerous sources both passively and

actively. Railways have become data rich because of these trends.

Wireless networking has enabled the data explosion by connecting devices without an expensive physical network infrastructure. Technologies such as 5G have allowed for faster connection of devices with transfer rates comparable to wired networks. This is extremely important in the railway, where space is often limited, and physical network improvements are expensive.

The data explosion in the railway sector has led to silos of data. In the absence of a centralized body controlling and managing the process at an enterprise level, different departments are often collecting information in different ways using a variety of formats. This often leads to duplication of data that hasn’t been consistently validated, and issues with access, accuracy and usability of the data across the organization. Under such scenarios, leveraging modern real-time data systems becomes impossible. Data management handles these problems through governance, architecture, and ownership, ensuring high-quality data is available across the organization.

DATA MANAGEMENT

WH Y ISN’T DATA TREATED LIKE OTH ER RAILWAY ASSETS, WITH APPROPRIATE GOVERNANCE, STORAGE AND MANAGEMENT?

Data governance is the foundation of data management. It creates a framework to ensure the availability, usability, and consistency of data, and outlines the processes and policies to ensure important data assets are formally managed. It ensures that trusted information is available and used for critical processes and decision making. Data governance policies outline rules for inputting and maintaining data, enforces those rules, and establishes data stewards and users to work within those rules.

Creating a scalable architecture for storing, arranging, and accessing data is essential for managing the volume of data generated by the railway. A robust data architecture creates repositories for retrieval and use of data by different departments, and standardized data structures to ensure the validity of the data. This allows different departments to access high-quality data at different periods of time through centralized repositories. With standardized data format, design values can be input in the same system as commissioned values and maintenance observations. Engineers can have access to lifecycle data from railway operations as input for more maintainable designs. Supply chain and procurement can have

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a better understanding of failure frequency and required inventory levels. All of this can be accomplished without duplication of effort.

Beyond the individual rail operator, the benefits of standardized data in the transit industry are transformational. A notable example of how governance of data structure can be successfully implemented is the General Transit Feed Specification (GTFS) – a widely accepted open data standard for sharing transit schedule, location and fees information. Since its inception in 2015, GTFS has played a significant role in the proliferation of “hundreds of useful and popular transit applications”5 by providing a common framework that application developers can use to develop user friendly and universal transit system tools and solutions. This has led to a significant improvement in overall transit system experience and fundamentally changed the way people use transit today. These benefits are possible because of data governance and standardized data formats.

TH E PROBLEM IS NOT TH E CAPABILITY, IT IS TH E LACK OF OWNERSH IP AND COMMUNICATION.

Centralized data repositories and formal data management are not new concepts. The railway has fallen short, specifically during the data explosion, in developing accountability for the data it generates. Other industries have created accountability by assigning a single entity to oversee data management in the organization. One example is Office Depot, which underwent a data management redesign to have a single entity accountable for the efficient and accurate storage of data in a centralized repository. The new structure allowed Office Depot to produce online catalogues forty percent faster than with the previous structure6.

DATA ANALYTICS

The full value of data is realized when abundant, high quality data is available as input to condition monitoring and predictive maintenance programs. These programs analyze operational data to predict when a failure will occur. Condition monitoring and predictive maintenance are the future of railway maintenance, offering significant benefits over schedule-based maintenance. As the railway harnesses the value of its data and implements condition monitoring and predictive maintenance, next generation maintenance programs become possible. These include up-time guarantees, risk-sharing models, and performance-based contracts.7

CONDITION MONITORING

Improvements in sensor technology have allowed for real-time, accurate measurements of rail systems. Physical phenomena such as heat, vibration, sound, rotational speed and axle stress can be measured to give insight into the condition of the system. New sensor technology also enables collection of data in ways that were previously unimaginable. For example, fibre-optic cables can be embedded in cable insulation to provide an accurate temperature profile of the cable. The temperature data can be used to determine the condition of cables and identify trouble areas that are at risk of failure. Lasers, combined with cameras, can be used to create detailed digital profiles of the rail and overhead contact wire. The digital profiles can be used to detect abnormal wear patterns and areas where track geometry is outside of tolerances, without the need for manual inspections. Voltage and current characteristics can be measured on motors and can be used to monitor the condition of the motor. All three of these measurement techniques collect valuable data on the condition of the system in near real-time and can be used to inform maintenance decisions.

Low cost electronics are now embedded in most railway equipment. These electronics often include monitoring functions that can report on equipment health in real time, generating alarms when the equipment is not operating as intended. These alarms provide additional data, allowing the railway to identify failures and dispatch staff to address the issue.

In condition monitoring, one parameter of a component is measured in real time and compared to critical thresholds. When the parameter exceeds the thresholds, the component is scheduled for maintenance. An example of this is in switch machines, where motor currents can be monitored and compared to a baseline value to identify abnormal conditions before a failure occurs. Once an abnormal condition is detected, maintenance can be dispatched to address the problem. Condition monitoring also provides insight on the troublesome component, ensuring maintenance staff have the correct parts and tools when they are dispatched for the repair. On a fully monitored system, maintenance programs based on condition monitoring are estimated to reduce maintenance costs by 15%.3

The future rail system is completely monitored in real time. Monitoring data will indicate when and why a piece of equipment has failed, or when there is excessive wear on a section of rail. However, monitoring the system only shows part of the picture.

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FUTURE READY MAINTENANCE

Predictive maintenance takes condition monitoring to the next level, using multiple data sources to predict when the component will fail. Predictive maintenance is possible because of advances in data analytics and machine learning technologies such as neural networks and reinforcement learning. Given the volumes of data in the railway, predictive maintenance can be thought of as the application of big data for the railway. Predictive maintenance is estimated to reduce maintenance costs by an additional 10% over a condition monitoring maintenance program.2

Knowing when a component will fail, with a reasonable level of certainty, allows railway operators to plan and optimize maintenance activities, reduce service disruptions and increase asset availability. Furthermore, the data collected for predictive maintenance can be used to engineer a more reliable railway.

Predictive maintenance paints a picture of the health of the entire railway. Railway operators can use this information to plan and optimize maintenance activities. For example, if a component is operating outside of its critical threshold, predictive maintenance can indicate if an immediate response is required. If an immediate response is not required, the component can remain in service until it is economical to complete maintenance.

Predictive maintenance minimizes the number of failures, improving overall service levels. For example, if a train door is predicted to fail, the train can be removed from service to address the issue. This maintenance eliminates a possible service disruption, improving the overall service level of the railway.

Predictive maintenance increases the availability of assets. With predictive maintenance, assets are removed from service less frequently and service is usually limited to replacing deteriorated components. This increases the amount of time the asset can remain in service compared to other types of maintenance. For trains, increased availability can significantly reduce capital costs because fewer trains are required to meet the same service level. Furthermore, fewer LRVs require a smaller footprint for maintenance and storage buildings, reducing capital investment.

The benefits of predictive maintenance extend beyond the maintenance program. Failure data can be analyzed on a system level, identifying component failures and contributing system characteristics. This allows for root-cause analyses and component redesign to improve the reliability of the entire system.

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BIG DATA DRIVING FUTURE TRENDS To be Future Ready, Engineers and Operators need to understand the value of data over the life of the system.

Design must be completed with the value of data as a consideration. Sensor networks and data architecture should be designed at the onset of the project. For existing systems, wireless sensor networks should be explored as a cost-effective method for acquiring data.

Operations must understand the value of data and create data governance and architecture to maintain high-quality and up-to-date data. Organizational structures should reflect the importance of data, assigning responsibility for data management.

Finally, the railway needs to rethink maintenance philosophies and challenge the status quo. Predictive maintenance is a fundamental change from current railway standards. Realizing the full benefits of predictive maintenance requires trust in data and a commitment to continual improvement.

The value of big data is not limited to data collected by the railway. Data can be gathered from services outside of transportation. Meteorological data can be used to determine if weather conditions contribute to system failures. Mobile phone data can be used to create accurate origin-destination maps, a key input to transit network planning. Major event schedules can be imported into transit operation schedules to adjust service levels to meet demand. A Future Ready railway will be prepared to use all types of data to improve reliability and service and reduce risk and increase innovation.

The future of the railway will be an era where the greatest asset is no longer the rollingstock, infrastructure or even people. Data will be the railway’s greatest asset.

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