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  • Writer's pictureElipsa

Avoiding the Perceived Obstacles of Fault Detection and Diagnostics

Implementing Fault Detection and Diagnostics (FDD) in your buildings can help reduce energy consumption and cut utility bills.  In addition, it can improve indoor air quality and comfort, increase equipment lifespan, and operational efficiencies.  However, despite the benefits, many are reluctant to move forward with implementation. Why? What are the perceived obstacles in implementing FDD in buildings?

Perceived obstacles include high initial costs, complexity and technical challenge, data management and quality, skill and expertise requirements, resistance to change, and ROI uncertainty.

Let’s tackle these and explain how Elipsa’s innovative approach helps remove these obstacles and enable implementation at scale.

High Initial Costs

Elipsa’s FDD solution is built off of it’s automated machine learning platform.  Through the use of AI, Elipsa automates a significant portion of the implementation process.  This automation means less setup time and therefore lower setup fees.


In order to implement FDD you need to have equipment and points.  For most systems, it takes a considerable amount of time to gather this information. Then it takes even more time to configure the equipment and associated points in an FDD system even before configuring what you will actually be monitoring.

Elipsa’s auto-tagging fixes this. With auto-tagging, all you need to do is export a list of your equipment and points. Using AI, Elipsa can then automatically determine the type of equipment and points configuring the FDD system in the process. This automated approach drastically reduces the time and cost to configure the FDD system with the equipment that will eventually be monitored.

Skill and Expertise Requirements

With equipment and points now configured in the FDD system, what are you going to monitor? For some FDD systems, you need to answer that question yourself.  Some systems require you to code individual rules. Others have allow you to pick from a list.  Both of these approaches requires a lot of time and knowledge. Elipsa takes a different approach.

AI Monitoring Templates

Elipsa AI Monitoring Templates

Imagine a menu of all possible components to monitor for a given piece of equipment. Each of those have a set of points to be monitored.  So, you know the points you need and with the system already configured you know what points you have.  With Elipsa, don’t bother selecting what you want to monitor, the system chooses for you. Automatically apply monitoring for all components that you have a sufficient number of points for.  Gone is the need for expertise of deciding which points to monitor and which components you have or do not.

Pure rules based systems monitor equipment based on defined thresholds.  You can utilize industry standard thresholds or select your own. This is a daunting task as you think about the level of experience needed to appropriately come up with the thresholds to monitor for.

With an AI-based approach there is no need for thresholds.  The AI learns the thresholds. More specifically, the AI learns normal operation to detect when the system is trending away from normal.  The end result is once again a much faster implementation but also removed is the obstacle of having highly skilled and experienced personnel doing the implementation.

Data Management and Quality

For advanced analytics, data is always a challenge and particularly access to that data.  Elipsa utilizes MQTT to integrate into numerous systems.  In addition, we have partnered with companies such as Tridium to directly access data from your Building Management System.

There are certainly buildings that are not connected. Installing sensors could certainly be cost prohibitive but it also poses the question of when do you have enough data. AI needs historical data to build a model.  So, what do you do if you do not have a long history of quality data?

Elipsa’s templates can get you started without historical data.  In fact, if builds your historical data set as the equipment runs.  Elipsa’s instantly employs ASHRAE 36 rules to be able to monitor equipment within an hour of streaming equipment data to Elipsa.  Within a few short weeks, enough data will have been received to build an AI monitoring model for your equipment’s components. This model creation is done automatically without additional configuration by the user.

Connectivity and quality are certainly issues but Elipsa has taken a partnership approach to go directly to the source and remove that burden from you.

Uncertain ROI

According to the US Department of Energy, buildings can save 3-9% per year on energy costs by implementing FDD.  The challenge is that where you are on the range depends a lot on your building.  From age of building and equipment to energy conservation measures that have been implemented. The savings range is fairly easy to calculate but the second piece of your return on investment is the investment.  This ties to the obstacles if initial costs and skilled employees to implement.  Those are often hard to calculate and as we said earlier, often considered too high.

The large amount of money and time to implement makes it hard to see a justifiable ROI.  However, if we can eliminate the complexities of implementation and in turn greatly lower the cost, that ROI becomes much more attainable.

Resistance to Change

This is an industry where things have been run the same way for a long time.  One can easily say that the resistance to implementing FDD is due to resistance to change. Really though, that is a catch all. With all the other perceived obstacles it’s easier to just avoid them and say you don’t want change.  In general though, change is easier to accept when it is easy.  By eliminating many of the obstacles discussed earlier, FDD is now easier to try and therefore it is easier to change.

We are at a point where change is now possible and hopefully soon to a point where it is more acceptable with Elipsa’s FDD solution.


There are obstacles in implementing any technology, especially new technology.  However, when something comes along that helps you avoid those obstacles you can finally implement at scale and reap the rewards. Elipsa’s platform is built on AI and built on simplicity.  This simplicity finally makes FDD, and the savings associated with it, a possibility paving the way for greater operational efficiencies and energy saving.


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