Railway Goes Smart with Predictive Maintenance and Industry 4.0 CMMS
The railway industry is a dynamic sector with a large amount of assets, maintenance and repair equipment, depots and stations spread across a wide territory. Good internal communication, fast reactions based on equipment geolocation data , high-quality maintenance planning and regular interventions are required to keep this massive machine work.
The first step towards a modern, next-gen asset and maintenance management is the adoption of a smart CMMS. Smart here implies inspired by the latest digital trends and supporting the creation and integration of predictive maintenance algorithms.
And if there is still someone asking, “Why should the railway industry go smart?”, the answer is more than simple: “Because Industry 4.0 ensures reliability and safety”.
In the future, reliable railway maintenance is expected to rely upon smart transportation systems and interconnected solutions such as predictive maintenance and integrated security tools in order to improve critical issues like safety, delays and overall system capacity. As a result, the usage of all assets, from tracks to trains through all involved machines and equipment, should be maximized to ensure the industry’s crucial role of a driving factor for sustainability and improved urban mobility.
Sensors are maybe the smallest but one of the most important details when dealing with sensible topics as traffic and trains control. The good news is that sensors are getting constantly cheaper and their implementation as a leading real-time data provider will generously increase. This will push condition-monitoring analysis to prevail over reactive-based approaches and irreversibly change the way assets are managed and maintained.
In the railway industry, equipment failures can have fatal consequences. For this, technicians cannot risk to be surprised by unplanned downtimes or problems. The entire sector is in constant search of improvements to deliver a better customer experience and maximize assets use potential. Sophisticated predictive maintenance combined with a modern CMMS is now available in the context of industry 4.0 and might be the solution the industry has been looking for. Finally an interconnected smart system can help to manage, maintain and connect tracks, rolling stocks, terminals, railroads and communications infrastructure.
Main Characteristics of a CMMS Tailored to the Specific Needs of the Railway Industry
The first and main characteristic of a railway industry CMMS is its ability to contribute to the identification of maintenance issues before they impact safety, operations or revenue The solution should collect, store and analyze data to prevent breakdowns and issue predictive maintenance algorithms in order to extend equipment life. A CMMS deployed at a railway company should combine all features and characteristics of a modern, next-gen CMMS as:
- simple to use,
- fast, reactive, flexible
- mobile application, accessible anytime and anywhere
- connectable to ERPs and IoT systems
- geolocation tool
- analytical tool
- unrestricted media upload
A step further would be the integration of an entire communication platform on the one hand to connect internally all employees from the different departments as accounting, operations, purchase and maintenance; and on the other hand to reach out externally and build a network of manufacturers, technicians and suppliers to exchange expertise and speed up operations.
Mobility Work for example is a Next Gen Maintenance Management Platform, providing its community with an integrated social media where maintenance professionals from all over the world can exchange.
Predictive Maintenance for Improved Railway Asset Performance
Today’s wide range of affordable sensors makes it easy to collect huge amounts of data from all possible systems at a single train and analyze it in real time in order to detect problems before they actually occur. Predictive maintenance implies the constant monitoring of a piece of equipment through the measuring of all relevant variables as temperature, vibrations, oil levels, etc. It is all about anticipating the optimal timing for maintenance, which enables faults to be identified proactively and eliminated with the necessary maintenance interventions.
When deployed correctly, predictive maintenance is a powerful tool, improving visibility into asset health, reducing unplanned downtime of equipment and minimizing the high cost of unscheduled maintenance.
If So, Then Why Has the Uptake of Predictive Data Analytics in the Rail Sector Been Slower Than Expected?
For technological specialist is obvious that the railway industry has all means to deploy an effective predictive maintenance strategy thanks to the advancement of the sensor technology and data analytics. However, for railway professionals the reality looks different.
In order to follow the numerous government rules for security and quality, a railway company has to implement new technologies into every single train of the fleet and this takes time…a lot of time. Most of the equipment is already maintained based on condition-monitorig data, which is a huge step forward for a traditionally conservative industry.
Other big hurdles disrupting PdM implementation are the infrastructures and climate conditions, which vary from place to place and make trains maintenance dependent on their specific environment.
Resources are another important premise when adopting predictive maintenance. Building an effective solution requires large amounts of data but also human resources and additional trainings.
Today, a temporarily solution would be the implementation of predictive maintenance only in certain cases where it is proven to reduce costs and downtime in the long-term.
Predictive maintenance and CMMS: How to Get the Best out of It
The most important move for the successful deployment of a predictive maintenance solution is the successful adoption of a smart, next-gen CMMS. The computerized maintenance system is the place where all condition monitoring data is stored and finally analyzed in the context of financial, spare parts, environmental, track layouts, rail defects, track category, area zones and equipment intervention history data to produce the first predictive algorithms. Basically, the CMMS gives raw data a concrete meaning by combining it with all possible current and historical information to understand both the state of the assets and how they are deteriorating over time.
Combining a CMMS and predictive maintenance into one seamless system reduces the risk of breakdowns, increases first-time-fixes and decreases maintenance cost. Today’s advanced CMMS analytic tools enable organizations to extract insights from data with great speed and accuracy optimizing availability and prolonging the life of assets.
IoT-Connected Trains for Happy Passengers
Another big trend emerging with the adoption of predictive analytics is the integration of big data and IoT (Internet of Things). For the railway industry this means to interconnect all objects and devices that previously weren’t part of a network and thus to enable the implementation of applications that increase safety, efficiency and ease of use.
Next-generation transportation systems will fundamentally transform the railway industry in two main ways: safety and passenger comfort. Safety was and is still the main element of all recent industry 4.0 applications and solutions when it comes to train management. Guidance, control, security and surveillance systems will drastically reduce the risk of collisions and regulate velocity. On the other hand advanced consumer technologies will maximize connectivity and allow passengers to continue their activities on smart devices whether travelling for business or pleasure. Furthermore, train-to-train communication in the cloud enables operators to transmit data about equipment, tracks and stations among themselves and improve passenger experience.
The rail customers from today expect a flawless service. With the rise of latest industry 4.0 trends, railway companies can now ensure that they are prepared and avoid the surprise of equipment downtime. Despite legacy infrastructure and the slow adoption of automation, the railway industry is clearly on its way to integrate predictive maintenance and big data. The recent advancements in sensors and condition monitoring technologies have led to continuous data collection and evaluation, significantly minimizing the number and cost of unscheduled outages.
Writer Bio: Ralitsa Peycheva
Ralitsa Peycheva is a technical content manager at Mobility Work (www.mobility-work.com), interested in latest machinery tools, technical maintenance, CMMS and big data; curious about new manufacturing methods; discovering, observing and admiring high-quality engineering.
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