Transportation systems are complex with respect to technology and operations due to the involvement of a wide range of human actors, organisations and technical solutions. There is a need to apply intelligent computerised systems for the operation and control of such complex environments, such as computerised traffic control systems for coordinating advanced transportation.

Industry 4.0 is enabled by smart systems and Internet-based solutions. Maintenance is one of the application areas of self-learning, and smart systems can predict failure and trigger maintenance by making use of the Internet of things (IoT).

There is no established path for success of any emerging technology, but creating a roadmap can help the rail and aviation industries to bring a more digital and connected future. The need for these industries to be smart is there because Industry 4.0, or the fourth generation of industrial activity, ensures reliability and safety to these sectors. With automation of the manufacturing industry, these sectors will realise efficiency, capacity and cost benefits of Industry 4.0. Enhanced industry-wide condition monitoring will also help reduce unplanned maintenance. Both sectors are in constant search for improvements to deliver better and secure customer experience.

Digital Railway Solution

The digital railway programme is focussed mainly on digital signalling technology, which aims to enhance safety and speed up train movement in a congested network. If all data from signalling, rolling stock and passenger traffic control systems is brought together on a common platform, the entire network will be able to communicate seamlessly and instantaneously. The key to digitisation is the interoperability of systems while retaining a critical approach to data security.

Rail service information could even be integrated with other transport modes, such as bus and taxi services, to guide passengers through smooth door-to-door journeys. Holistic data management could lead to the transformational change in real-time intelligent traffic management and in-cab signalling. This could improve customer satisfaction, with station information systems and personalised messaging providing passengers with all the relevant information they need.

Digitisation of Railways Includes:

• B-scan ultrasonic rail flaw detection (both non-stop and stop-and-verify systems) and track inspection with automated high-speed test trains.
• Train control system levels 2 and 3 for high-density routes to increase network capacity and maintain the required safety standards.
• Increased surveillance of personnel with both interior and exterior locomotive-mounted video surveillance to improve monitoring.
• Track-laying machines for mechanisation of construction.
• Electrification through machines such as self-propelled overhead electrification laying trains.
• Complete train scanners for improved diagnostics and maintenance.
• Use of distributed power to improve the efficiency of train operations with coordinated acceleration and deceleration.
• Establishment of smart railway stations by implementing access control at entry points.
• e-ticketing with services such as infotainment and app-based systems.
• Use of training simulators and virtual reality (VR) training systems to improve personnel capabilities.

Digital Twin— Digital platform for railways and airways

A digital twin refers to a virtual replica of a physical asset, like an aircraft engine or a rail engine. It is a vital element of the digital rail solution that is continually updated as per the rail network. It enables engineers to test detailed what-if scenarios that could help in decision-making around the planning of enhancement and maintenance programmes. It could identify the most-valued solution that would have the greatest efficiencies and minimise disruptions.

Role of sensors in predictive maintenance

Sensors use a reaction-based approach to manage and maintain an asset and maximise its use potential. A wide range of sensors is available to collect huge amounts of data from all possible systems of a single train and then analyse it in real time to detect problems before these actually occur. Constant monitoring of equipment through the measuring of all relevant variables such as temperature, vibrations, oil levels and the like help anticipate the optimal timing for maintenance. 

It enables identification of faults proactively and elimination of necessary maintenance interventions. Predictive maintenance is a powerful tool that helps track asset health, reduce unplanned downtime of equipment and minimise the high cost of unscheduled maintenance. Optical and tactile sensors such as light curtains, camera systems and dynamic pressure-sensitive mats are suited to monitoring areas near rail vehicles. Good internal communication, fast reactions based on equipment geolocation data, high-quality maintenance planning and regular interventions are required to keep massive rail networks working.

Predictive maintenance and CMMS 

Modern, next-generation asset and maintenance management starts with the adoption of a smart computerised maintenance management system (CMMS). Reliable railway maintenance is required to improve critical issues like safety, delays and overall system capacity. It is expected to rely on smart transportation systems and interconnected solutions such as predictive maintenance. An interconnected CMMS can help maintain, manage and connect tracks, terminals, rolling stocks and communications infrastructure. It can identify maintenance issues before these impact safety, operations or revenue. It collects, stores and analyses data to prevent breakdowns and issue predictive maintenance algorithms to extend equipment life.

A reliable CMMS should be user-friendly, fast, reactive and flexible. It should also have a mobile application for anytime access, connectable to ERPs and IoT systems, geolocation tool, an analytical tool that supports unrestricted media upload and so on. Different departments such as accounting, operations, purchase and maintenance should also be connected to the entire communication platform. It can work as a network for manufacturers, technicians and suppliers to exchange expertise and speed up operations. Advanced CMMS analytic tools enable organisations to analyse data with great speed and accuracy, to optimise availability and increase the life of assets.

The IoT-connected trains

The IoT can interconnect all objects and devices that were previously not part of a network for predictive analytics. Its application increases safety, efficiency and ease of use with train management systems. Control and surveillance systems reduce the risk of collisions and regulate speed. Advanced consumer technologies help maximise connectivity and allow passengers to continue their activities on smart devices while travelling. Train-to-train communication through the cloud enables operators to transmit data about equipment, tracks and stations among themselves.

The IoT enables monitoring of areas on railway crossings remotely, such as barrier operations and end positions, switch end positions, space between barriers, system operations, connections and signals. This allows users to accelerate their projects, from engineering and runtime to maintenance with fast detection and localisation of errors and faults.

Here are some potential use cases presented by rail operators for using IoT to create a connected railway.

• A journey planner application could recommend the fastest or most comfortable current trip allowing for road conditions to the station, live train times, available car parking capacities, passenger loading, etc., allowing passengers to make informed choices about what option will provide them with the best experience according to their personal circumstances, for example whether it is more important to have the shortest journey time, or to be guaranteed a seat. Allowing the inclusion of historic data will enable evaluation not only for a current trip, but also in a predictive way for a trip planned at a future date, based upon what is normal for the planned day and time of travel.
• Combining passenger loading information from trains with social networking apps will help spread demand peaks. The same base information shared at a terminus can help in selecting the destination platform offering the most efficient passenger egress considering the loadings of other inbound trains, whilst sharing the same information on the train can produce a more even distribution of passengers within the carriages, potentially allowing standing passengers to find a seat.
• Combining status information from diverse on-board public-facing assets such as toilets, food car chillers and ovens, and presenting it to service organizations with current positional information can improve the customer experience and reduce the penalty costs associated with having these assets out of service.
• Intelligent closed-circuit TV cameras not only provide a record of events in case of an incident, they actively provide real-time alarms of the occurrence of potential problems, allowing more timely intervention responses and potentially reducing service outages.
• Information concerning categorization of faults can be analyzed across multiple assets, even multiple operators, to spot trends and identify areas for preventative maintenance.

• The automation of toilets can significantly reduce the cost incurred by the train operator and, at the same time, provide a better service to passengers who will less likely find a toilet out of order. Currently, most train operators are unable to determine the status of the on-board toilets in real time and a significant amount of manual checking is required.
• Management of the video recordings on board. Many rail operators have to send personnel on board their trains to manually pick up the hard drive when video recordings are requested by a law enforcement agency for investigation of an incident.
• Food and drinks can be easily refilled at the upcoming station if data is available in real time regarding the items sold.
• Temperature can be remotely controlled to avoid issues with refrigerators that might not be working at all times but whose temperature is critical to preserve the food quality over time.
• Predictive and preventive maintenance can dramatically increase the percentage of times a train is in use rather than sitting in a maintenance or repair shop, and also improve the passenger experience and safety.

Big Data analytics for smart railways

The complete Big Data architecture includes the IoT and cloud computing devices. These work together to create smart railways that have self-learning capabilities to predict failure, make diagnoses and trigger maintenance actions. The architecture utilises multiple data sources to extract relevant information. It helps users to know what happened when, so they can go back and do the root cause analysis from the data, and take appropriate corrective action. Big Data analytics in railways lead to predictive analytics and make decisions based on huge amounts of data. These involve data collection, analysis, visualisation and decision-making for assets.

Estimation of the remaining useful life of an asset to ascertain the probability of its mission accomplishment is key to the success for any organisation. The railway domain can achieve data interconnection via the train bus where most railway sub-systems and their respective sensors are accessible for global optimisation.

With the increase in demand for more passenger rail services and greater volumes of cargo trains, use of data and its analysis will become a primary asset for the railway sector. Millions of data points captured from sensors on critical train components will help detect impending part defects, ensuring maintenance before a defect occurs. This will improve reliability of the system infrastructure for many years by remote monitoring of location and condition of all vehicles.

Safety is a key area of concentration

Safety is, of course, a primary element of IoT applications and solutions when it comes to train management. One safety use case is on-board train location and detection systems that enable trains to be aware of the positions of other trains. This reduces the risk of collisions while allowing trains to operate safely in close proximity to one another.

Speed monitoring and control is another important safety application. Systems have been developed that can display train velocity for drivers and report speeds back to central control systems. On-board monitoring systems are interconnected with outdoor signalling systems that can regulate train speeds or even remotely command trains to stop based on track conditions, the positions of switches, the presence of other trains on the track and other factors.

There are three major systems within railroads that automation and the IoT can bring significant benefits: signalling, interlocking and level crossings control.

1. Signalling systems control the movement of a train by remotely adjusting train speed and braking. More traditional signalling systems are based on radio-frequency identification along the train track, but wireless train to ground signalling is getting more and more common in both railroad and metro systems.
2. Interlocking avoids conflicting movements on the tracks at junctions and crossings by using red and green light signals. The interlocking system works in conjunction with the signalling system to prevent a train from getting a signal to proceed if the route is proven to be unsafe. The IoT can further improve the system’s level of automation and its integration with the signalling system.
3. Level crossings control is the third system that impacts safety on railroads. Accidents related to level crossings represent 30% of all railway fatalities in the EU. IoT can help decrease those statistics by deploying cameras and sensors for increased safety.

IR & OMRS: New Age Technology for Predictive Maintenance

Indian Railways (IR) is moving towards the adoption of automation and instrumentation in its maintenance practices for detecting defects/deficiencies in rolling assets. The objective is to achieve machine-assisted automatic identification of defects in the Rolling Stock. This will lead to a paradigm shift in maintenance practices of Rolling Stock of Indian Railways from ‘Time Based Maintenance’ to ‘Condition Based Predictive Maintenance’ with a view to enhance reliability and availability along with improved safety of Rolling Stock during run.

For this, On-line Monitoring of Rolling Stock System (OMRS) is being adopted in Indian Railways. OMRS is a way-side inspection system consisting of Acoustic Bearing Detector (ABD) or Rail Bearing Acoustic Monitor (RailBAM) and Wheel Impact Load Detector (WILD)/Wheel Condition Monitor (WCM) to detect the faults in the bearings and wheels of rolling asset. This is an automated system for detecting defective wheels and bearings, and catching the same before it fails, thus resulting in efficient utilization of the coaches, wagons & locomotives. OMRS monitors the health of each Rolling Stock of the train in order to identify defective bearings & wheels. Defect report generation and alert communications takes place in real time for taking corrective action, accordingly.

The current practice of inspection of Rolling stock over Indian Railways is largely based on manual inspection, which is either track side Rolling-in-Examination or pit examination of Rolling Stock in stationary or slow moving condition. The visual inspections are done by trained manpower either in a pit or track side location but this relies on the individual judgment.  Therefore, an automated defect detection system viz. OMRS is being adopted by Indian Railways which consists of following sub-systems:

• Acoustic Bearing Detector (ABD)/ Bearing Acoustic Monitor (RailBAM) gives an early warning on possible defects in the bearing box, before reaching the stage of hot box.
• Wheel Impact Load Detector (WILD)/Wheel Condition Monitor (WCM) system measures the wheel impacts on tracks to identify the flat surface on wheels in Rolling Stock. This system is based on Accelerometer device to measure the wheel impacts.
• PhotoTAG system is used for vehicle identification using Visual (photographic) identification technique.

Encouraged by the results of deployment of OMRS, including some critical detection which could have potentially been cause of an accident, not otherwise detectable by normal maintenance procedure, Indian Railways is now going ahead with greater adoption of track side based maintenance systems with an aim towards predictive maintenance. Further, moving towards predictive maintenance practices in yards, Indian Railways is envisaging to convert its ‘freight examination yards’ into technology driven ‘Smart Yards’ for automatic detection of faults/defects/deficiencies in freight wagons. These Smart Yards will predict anomalies like Hot Wheel Hot Axle, defective bearings, defective wheels, hanging/loose/missing parts etc. long before any failure actually happens. Smart Yards will be equipped with various automated technology driven systems including OMRS, Hot Box Detector, Wheel Profile Recorder and Machine Vision Equipments etc.

The concept of smart yard is to use modern repair facilities, infrastructure, tools, automatic defect detection equipments and digital technology to enhance safety, reliability and productivity in freight trains operation. The automatic defect detection equipments of Smart Yard shall provide advance data about hot axles and wheels, wheel flats, wheel profile & diameter, load imbalance, spring breakage, loose and hanging parts, wear condition of brake blocks etc. even before the rake arrives at the maintenance yard. It will then use this information for objective fault assessment and proactive staffing, thereby, reducing turn-around time while boosting safety and improving productivity.

Status of implementation of Smart Yard:

• Initially in 1st phase, 40 identified yards will be converted into Smart Yards.
• COFMOW (a unit of Indian Railways) has been nominated for carrying out the overall work of Smart Yards.

The implementation of aforementioned technology driven automatic predictive maintenance practices for up-keep of Rolling Stock not only will benefit Indian Railways on account of efficient/safe operation of trains but will also benefit on economic ground.

Conclusion

The railway industry is on its way to integrate predictive maintenance and Big Data. Recent advancements in sensors and condition monitoring technologies have led to continuous data collection and evaluation, significantly minimising the number and cost of unscheduled maintenance.

Most significant improvements have been evidenced by more informative and user-friendly websites, mobile applications for real-time information about vehicles in motion, and e-ticket purchases and timetable information implemented at stations and stops. With the rise of Industry 4.0, railway companies can now ensure that they are prepared to avoid the surprise of equipment downtime.

More technologies to meet needs of the railways

Researchers have developed a technique known as frequency-selective coating of window panes to solve the problem of low mobile Internet signals. These panes are provided with a transparent electrically-conductive layer consisting of metals or metal oxides. Metallic coating of the windows is vaporised along lines in a special structure by a laser to pass certain frequency ranges unobstructed.

The VR lab at GE Transportation’s John F. Welch Technology Centre, Bengaluru, have facilitated a 3D VR environment for inspection. It tracks the motion of inspectors through specially-designed wearable goggles and infrared (IR) cameras placed in the room. The VR controller allows users to interact with individual components. The environment helps teams at different locations collaborate and review products that are under development. The lab can also be used as a training ground for service engineers who work on products in the field.

Locotrol distributed power system is a control and communication system that enables coordinated braking and traction power distribution between lead and remote engines for faster stopping times and shorter stopping distances. Locovision system through superior image quality cameras and real-time data processing monitors wayside assets, measures track gauges and detects intruders. It stores all information in a hardware infrastructure to help avoid major asset repairs and fines. The rail integrity monitor employs innovative technology with mounted sensors under the locomotive, continuously testing rail integrity in real time.

Automatic train control systems continuously monitor all train movements to provide fail-safe signalling. Operation of railways is centrally-monitored and controlled through operations control systems. Supervision systems, such as CCTVs or emergency telephones, also contribute to safety and enhanced comfort.

Signalling systems and railway automation solutions are crucial to detect and signal whether line sections are clear or occupied. There are three grades of automation and train control systems, namely, partially automated (supervised by a driver), highly automated (reduced driver supervision) and fully automated (the system is responsible).

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