Predictive Maintenance 4.0 reshaping rolling stock Asset Management

Cornerstone of the 4th industrial revolution, predictive maintenance 4.0 is a fast growing market of IoT-enabled intelligent sensors and data communication technologies built for machine condition monitoring applications. In any industry, extending the lifespan of your equipment and tools is a great way of cutting down on waste and can save businesses money by reducing the amount spent on equipment repair and replacement. However, until fairly recently it has been an extremely complex task to predict when vehicles, equipment, and tools are going to break down and need to be repaired or replaced.  The ability to work out, based on data collected from in-vehicle and wayside sensors, when a vehicle or piece of equipment is likely to malfunction helps the operator avoid unplanned repairs or downtown. With the introduction and adoption of Internet of Things (IoT) enabled monitoring equipment, in-vehicle systems and components, mass transit operators of all kinds are beginning to put real-time data collected from these sensors to good use. This is especially true in the rail industry, where predictive maintenance is causing a revolution in rolling stock asset management.

Rail Predictive Maintenance 4.0

In an age where trains are becoming faster and sleeker, rail travel providers need to ensure that the vehicles, parts, and equipment being used to build and maintain their transit networks are doing so at optimal levels. Using broken, ineffective or outdated equipment can not only slow down any maintenance and repair efforts but also create hazards from being ineffective at their purpose. In order to guarantee their builders and repairmen are fitting high-quality parts with equipment that is in good condition, railway operators have been turning to predictive maintenance 4.0 systems to monitor and gather data on their rolling stock in order to identify any issues in need of repair or replacing. 

Rolling Stock and IoT

 

Rolling stock is the term used in the railway industry to refer to assets that move on a railway; train carriages, for example, would be classes as rolling stock. The term has since expanded since its early use and now incorporates wheeled vehicles associated with business. When dealing with fleets of trains, railway operators need to ensure they have some form of asset management in place so as to ensure that the train carriages they use for their service stay well maintained and ready to be called into the field. This is where IoT asset management solutions come in. The key to enhancing and optimizing asset management is through the use of data collected through IoT sensors and transponders that can then be evaluated to understand the best management methods for different assets.

 

There are several reasons why rail operators are looking to adopting IoT technologies into their operational and maintenance systems. Firstly, real-time data gathering from connected sensors and transponders allows operators to monitor and report both the condition and integrity of vehicles, parts and tools. Secondly, Remote monitoring of these systems means that data can be accessed through any device connected to the network, meaning employees are able to see real-time data being updated live on their smartphones or tablets. And lastly, in-vehicle communication gateways and onboard train computers are deployed to integrate real-time data communication between train and ground and to provide centralized intelligent control and management for onboard systems such as video surveillance, passenger information systems, temperature / door sensors and so on. However, it is predictive maintenance that is bringing some of the biggest impacts to rolling stock asset management. Let’s now take a look at how it works in order to better understand the benefits it is bringing the rail industry.

Predictive Maintenance 4.0 Key Benefits

 

Predictive maintenance is a function of IoT sensors that enables operators to receive data from tools, vehicles, and equipment from which they can predict with increased accuracy how and when something may break down or malfunction. By using this data to see how well a particular tool or vehicle is operating, operators can compare and combine data from environmental and operational sensors with previous maintenance records and route histories to calculate when and how different parts may become in need of maintenance and repair. With an understanding of how this data can be collected and acted upon, rail operators can then turn their attention to keeping their assets and equipment in optimal condition so as to provide the best possible service. With increased equipment and vehicle efficiency comes great numbers of travelers and so out-of-date, worn down assets will need to be revitalized in order to cope with growing use.

 

Predictive maintenance 4.0 is also optimizing asset management through the use of planned downtime. With the help of live data feeds from connected assets helping providers assess when train carriages are likely to break down, maintenance downtime can be planned ahead of time. This allows operators to work around schedules to plan maintenance downtime to mitigate its effect on train services while helping to keep rolling stock in optimal operational conditions. Additionally, the combined use of data from IoT vehicle and environmental sensors with historic maintenance and repair records can help identify the root causes of many issues faced by modern train carriages. This, in turn, saves money on unnecessary and expensive “temporary fixes” that can eventually to larger problems further on down the line. Once you understand how predictive maintenance works and how it is being used, it’s easy to see why more and more industries are being revolutionized by IoT-enabled devices and connectivity.

 

The Future Of Asset Management?

 

With the swift and growing adoption of IoT-enabled devices and systems, the power of data to transform and enhance the way we do business has become evidently clear. As our computing systems become more and more powerful, it becomes easy to imagine a future where increasingly sensitive equipment is able to send incredibly detailed data, not only about operational performance and environmental conditions, but also details regarding the integrity of a particular asset’s main structural material. In the case of a train carriage, this would entail data being received about the structural integrity of aluminum and how it performs in weather conditions such as those predicted for later that day, for example. This incredibly detailed data could then be used in predictive maintenance to give even more information on how and when parts are likely to malfunction and even suggest what may have caused the issues based on diagnostic reports from on-board and track-side sensors.

 

It is also easy to see how predictive maintenance 4.0 could also walk in hand with automated repair systems, whereby operators integrate their asset management systems to further increase efficiency and improve customer service while also cutting down on unplanned down time due to break downs or unexpected repair work being needed. With these systems working hand-in-hand, railway operators are then enabling themselves to create quicker services with better schedule adherence due to vehicles and equipment operating at optimal efficiency. It is even perceivable that repairs and maintenance down time itself could reduce as a result of predictive maintenance due to the equipment used in their maintenance being better maintained. There is no doubt that further research, development, and funding will go into predictive maintenance and other IoT technologies, but only time will tell how the various industries around the world adopt and make use of it.

Predictive Maintenance 4.0 reshaping rolling stock Asset Management was last modified: February 12th, 2018 by Rick Spencer
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