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Taking Remote Asset Management to the Next Level: Proactive Monitoring with AI for IoT

Updated: Oct 11, 2021


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By 2025, the remote asset management industry is expected to double in size. The major factors driving the growth include the adoption of IoT-enabled remote asset management solutions to manage assets efficiency, decreasing costs of IoT-based sensors, and the use of predictive maintenance to boost the adoption of remote asset management.


With the rapid adoption of connected devices to monitor assets remotely in real-time and analyze the downtime proactively, these solutions are gaining traction all over the globe.


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Remote Asset Management has acted as a bit of a springboard for successful IoT deployments, effectively proving a successful proof of concept as to the value that IoT can bring to an organization.


Like many new technologies or concepts, many have taken a crawl, walk, run approach to asset monitoring.


the crawl, walk, run approach to remote asset management


Crawl: Real-Time Location Tracking


Initially, the ability to provide real-time location tracking of equipment solved a critical problem in the industrial world.


As one example, the heavy equipment rental market in the US alone was $42.5 billion in 2021. That equates to a lot of expensive machinery deployed at various physical locations across the country. IoT-enabled remote monitoring tools enable rental companies to have an accurate view of where all of their equipment resides at all times.


Even outside of the rental business, companies owning or leasing a vast array of equipment for industrial use struggled to fully track where things were located. As you can imagine, misplacing something of that magnitude results in millions of dollars of losses so the value of IoT and Remote Asset Management was obvious.


Walk: Real-Time Condition Based Asset Monitoring


With a firm grasp on asset location, companies were able to move on to asset monitoring. Through sensors and remote asset management platforms, companies gained the ability to see how their equipment was performing in real-time.


What is the current flow rate of a water pump in Texas, or the RPMs of a wind turbine in California, or simply the battery level of telco equipment in Kansas?


Remote monitoring now gave a real-time view into the recent past performance of your equipment that previously required a tech to be deployed. Organizations can now visualize and monitor an entire fleet of equipment from a single location.


Run: Intelligent Asset Monitoring with Predictive Maintenance


With these two benefits now realized, Industrial IoT users are ready to go from walk to run with the addition of predictive maintenance.


Some of the key drivers to the adoption of predictive maintenance extended machine useful life and cost savings. According to the US Department of Energy, predictive maintenance can save an additional 8-12% over preventative maintenance and up to 40% over reactive maintenance.


Much has been written about predictive maintenance and many claim to utilize it but to be clear, condition-based monitoring is not predictive maintenance. Condition monitoring does not tell you what will happen but instead, it tells you what recently happened.


Connected devices in the field have proven successful at providing real-time views into the current state of a system but even the current state of a system is simply just the most recent historical view. Visualizing the past or deploying rules-based approaches still leaves industrial IoT users in a reactive state.


Instead, these historical views can be used by machine learning to learn and predict what will happen, finally enabling asset management tools to become intelligent asset monitoring tools.


Many organizations are actively seeking to start this run stage of asset management but the largest hurdle still exists in the form of complicated AI solutions making it difficult to build and deploy models without hard-to-come-by and expensive internal expertise.


Turning AI from Promise into Reality


The promise of AI has floated around the industry for years but the complexities of existing systems and a lack of internal expertise have prevented organizations from turning that promise into reality.


Elipsa's automated AI solutions help operators harness the power of their machine’s data in order to drive operational success at any scale.


Industrial IoT success is a function of the ecosystem that we create. No one can go it alone. As such, we partner with existing Remote Asset Management tools, small and large, to enable their transition from Asset Management to Intelligent Asset Monitoring.


To learn more about how to deploy predictive maintenance as part of your solution, book a demo here.