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Automating Predictive Maintenance Using Internet-Connected Sensors in Railway Industry


Introduction
The railway plays an important role in facilitating sustainable economic growth by connecting people and communities and providing a means of transport for people and goods. It has an advantage over road on the haulage of heavy bulk freight like coal and aggregates. It compares well with road and air on fast, long-distance passenger journeys; and it is competitive with road on the distribution of goods in the intermodal sector.
The business processes for industry, which includes planning, operations, engineering, and maintenance are the major means of asset optimization. All these form a part of asset management depending on the availability of vital data to process and predict the potential future breakdowns.
Industrial railways transport bulk assets like clay or coal to an interchange point, called an exchange siding, with a main line railway, onwards from where it would be transported to its final destination. Industrial Railway, not necessarily, have to carry only industrial equipment's. In some countries, it is also used to carry passengers from industry to working site and vice versa.

Major Problems occurring in today's Railways Industry:
a) Cost containment: Rail manufacturing is a high-risk industry with comparatively low marginal profit. Costs containing and upholding flexibility are essential since economic and operational conditions can change intensely over the extensive lifecycles in rail.
b) Absence of industrywide ideals and interoperability: Each country has its own arrangements for rail transportation and inconsistency among the several information-systems and services between trains, creates an intricate networking environment. Besides, it is challenging to provide reliable services or facilities among various rail systems through several nations.
c) Asset Maintenance & Optimizing Productivity: One of the biggest challenge is to optimize the efficiency in operations which has always been marred with unexpected break-downs and failures.
This is where Predictive Analytics comes into picture as a feasible solution.

What is Predictive Analytics?
Predictive maintenance is business intelligence technology, which helps organizations to predict the future trends based on the historical analysis for effective decision-making and to improve business and help regulate the situation of operating assets with the aim to foresee when maintenance should be performed. Developing a real time predictive analytics can ensure peak asset performance and reduced downtime. Asset optimization could be achieved by deploying sensors that offer the active and condition and position of all important assets.


Purpose of Predictive Maintenance in the given scenario:
The main purpose is to agree towards appropriate planning of predictive and corrective maintenance and to prevent sudden asset failures. Using this, it can be decided which equipment requires maintenance and repairs can be better scheduled and have various other probable benefits which comprise increased asset-lifespan, better plant safety and less mishaps without harmful effect on environment and improved spare-parts management.
Automating predictive maintenance using internet-connected sensors is the way to transform how businesses operate. By means of internet-connected sensors (IOS) embedded into the machines is entirely altering machine maintenance practices. They are used to predict future asset breakdowns and thereby increasing asset efficiency and minimizing losses. It benefits all asset owners and creates new chances for service providers and manufacturers.

What is Internet of Sensor (IOS)?
Sensors enable the physical world to interact with computers, providing a much richer assortment of data than is available via manual input. Sensors are used in a various set of applications like mobiles, automotive systems, industrial control, health-care industry, lubricant assessment and type of weather monitoring.  IOS is where sensors support all types of equipment and machines to coordinate and communicate with each other through the information network.
It creates the ability to collect data from a broad range of devices and that data can be accessed through cloud, analyzed using big data techniques, using RCM, CCTV, CLOUD, MOBILE sensors etc.


The main know-hows behind IOS centered predictive maintenance which gathers data from various sensors and accomplishes proximate real-time analysis is to define when equipment is at possibility of failure. The platform is adept in simultaneously meting out the operational characteristics of several machines, individually prepared with its own sensor group. This method of predictive maintenance makes it probable to avoid preventable maintenance and greatly reduces maintenance cycle times and also minimizes the risks of premature break-downs which means industries which operates machine-intensive methods which provide more value to their investments.

Now, let's take an example to understand the use of Internet sensors in Railroad Industry.
A critical safety issue is correct timing for crossing gates at railroad highway grade crossings. If the warning signals and crossing gates are activated too soon, impatient people/motorists may try to cross in spite of the signal which may result in a train-car collision. If the system is activated too late, again a serious accident may occur.
Consequently, the timing of grade crossing warning activation is critical. On rail lines with a mix of traffic speeds such as slow cargo trains and fast passenger sleeper trains and the need for train-speed dependent warning systems is particularly important. So, here the Fiber-optic sensors are used where train presence and speed detection can also be performed by measuring how the light signal passing through the rail-bonded fiber sensor is affected by a train passing over the rail. The light transmission through a fiber sensor is monitored while a train periodically passes over a section of fiber-bonded Rail. By novel fiber sensors and laser detection systems, there are several methods by which train presence and speed detection could be performed and safety can be guaranteed.

Benefits of Sensors:
a) Sensors will provide details about how goods are affected during transport. This information will help companies reduce damage and shrinkage.
b) Sensors will continue to become more advanced. No longer limited to just location, sensors can measure humidity, temperature, angle of inclination, and much more. The more types of metrics that sensors can collect, the greater the impact on the industry.
c) Sensors will alert them of actual conditions and connectivity will allow to communicate those situations and conditions instantaneously with the help of Internet.

IOS extends the effectiveness of Internet of things (IOT) by providing better connectivity between assets and systems using ever evolving Internet technology and smart network. It makes the remote condition monitoring effortless and makes the decision making much more efficient. Thus we can say that exploring the horizons of IOS for asset maintenance can truly transform the face of the industry.

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