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The Evolution of Asset Management: From Eye Inspections to Sensors to Big Data

Oct 11, 2018
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Rail car being placed on (or removed from) the tracks.

Probably the most significant thing to evolve asset management is the ability to track all your assets and have them send you updates on their condition. By using sensors to monitor a specific asset, automatically sending updates to your supervisory control and data acquisition (SCADA) system and generating work orders, you can increase the responsiveness of your operations. And after enough of that data is tracked, you can turn it into predictive maintenance.

Let’s take a quick recap of the different asset management/maintenance methodologies in use today:

  • Reactive – only after the asset fails
  • Preventative – based on time, mileage, usage, sometimes before the set manufacturer recommendations
  • Predictive – applying statistical analysis (based on data you've tracked over time) to pinpoint optimal timing
  • Condition-based (related to Predictive) – as the need arises, based on an indication of deteriorating performance or increased probability of upcoming failure, further complemented by real-time alerts leveraging sensors and IoT, as pre-determined thresholds are exceeded

Where We Were and Where We Are – The Advancements of EAM

Manual inspections of axle integrity were complemented in the 1930s by more advanced electrical and magnetic particle tests to help identify cracks. By the 1950s, early forms of ultrasonic testing were used on rolling stock axles to more accurately discover faults so repairs could be performed before disaster struck.

Computer advances in the 1990s increased our ability to track more data than ever before and gave us a new ability to monitor a wider range of precursors of asset failure such as excessive vibrations, unusual temperatures and so on.

As we progress further into the digital age and with data becoming the stronghold for understanding your operations, the maintenance regimes within public transit are changing. For example, individual components of a switch can be tracked. Before, you used to track the switch as a whole, and if something triggered an alert, an inspector would have to go out and manually determine the problem. Now, you can determine if it’s one of the sub-components of the switch (left-hand turnout, right-hand turnout, right-hand track switch, or the frog).

Furthermore, seeing the history of the sub-components allows you to discover the best corrective measures. Is there a tendency to use your right-hand turnout more than the left? If this is the case, maybe you need to change the frequency of inspection or maintenance for that particular turnout to combat quicker erosion.

React to an Alert in Seconds

The sophistication of data analysis and technology has advanced asset management. So much so, you can specify individual components, allowing you to track just about anything and analyze it for future use. These systems also track your assets in real-time, enabling you to act on an alert in an instant.

Because your assets are monitored with sensors, that data is automatically being sent to your network. When your SCADA system (or something similar) detects a fault, it sends the information to your servers. Then your enterprise asset management system automatically generates alerts to the maintenance team for a corresponding action and saves the data to be analyzed at a later date. If the alert is critical, you will be able to address it immediately.

But there are times where a non-critical asset will reach a threshold that raises an alert but doesn’t require immediate attention. This asset will still be fixed, but it can be noted and tracked into your regular maintenance schedule. This all demonstrates that, for best results, you want to combine methods for a more dynamic asset management structure, based on criticality.

3 Steps to the Holy Grail of Maintenance

Following the journey of asset management, you can see it’s progressed a ton. Depending on your technology or your maintenance program, there is still a need for all of these techniques. But to be fully optimized, here are three steps to achieve the Holy Grail of maintenance.  

Consolidate fault data into your EAM system. This will automatically trigger fault codes and work orders on your assets. Make sure to determine which assets are mission critical for immediate response
Determine your strategy to compile your data so you can begin tracking the patterns in the data  
Use the data to get a deeper, more realized understanding of your infrastructure’s health patterns to optimize your maintenance program. Factor in/Use condition-based and predictive maintenance to strengthen your maintenance program

The asset management world is changing because of data and technology. We are in the midst of a maintenance evolution with condition-based and predictive maintenance as the main outcomes. But, can you predict where asset management will go next? 

Want to hear what others are saying about EAM? Listen to RTD's Dave Genova discuss the importance of asset management and capital planning.


 
Marcelo Bravo has dedicated his entire career to rail and transit with over 25 years of experience in the industry. Previous responsibilities have included the delivery of passenger rail cars from cradle to grave, Enterprise Asset Management (EAM) software, and management consulting to transit authorities and railroads, in both North America and abroad. As the Industry Solutions Manager for Rail, Marcelo is in charge of the rail market strategy for North America, which encompasses the Trapeze Rail Enterprise range of offerings.
 
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