ASHRAE 36 Fault Detection Made Simple
Pretty much everyone we talk to in the building space knows ASHRAE 36, the guidelines for High-Performance of Operations for HVAC Systems.
If you open the document and look at the scope of the document it lists:
- This guideline provides detailed sequences of operation for HVAC systems
- This guideline describes functional tests that, when performed, will confirm implementation of the sequences of operation
So, it is no surprise that when you ask people what ASHRAE 36 encompasses, the answer is control sequences to optimize the performance of your HVAC systems. What many do not realize is within the same guideline is a list of rules for continuously monitoring the performance of these HVAC systems known as Automatic Fault Detection and Diagnostics (AFDD).
ASHRAE 36 AFDD
These rules offer insights into which elements are most important to track and monitor in order to ensure that your systems are running optimally. They offer a best of breed approach to detecting potential faults.
many think the implementation of these rules are unattainable
However, for those that we talk to who do understand that ASHRAE 36 goes beyond control sequences and offers AFDD, many think the implementation of these rules are unattainable.
Put another way, they often comment that the rules cannot be implemented if you also do no take the time to implement the control sequences themselves.
A rules based approach typically monitors against a threshold. For example, when monitoring an AHU, the guidelines say to alert someone when the current duct static pressure is more than 25 Pa (or 0.1") below the average duct static pressure. 25 Pa was deemed to be the appropriate threshold after extensive testing but is 25 Pa appropriate for all AHUs and is it attainable if you do not implement the defined AHU control sequences on that device?
AI-Based Monitoring Built Off of ASHRAE 36
This is the challenge people see with ASHRAE 36 and the overall challenge with rules based approaches to FDD. So how do we overcome this?
This is where AI comes in. At Elipsa, we follow the same guidelines of ASHRAE 36 in terms of points being monitored for specific equipment types and components. However, with the use of AI, we do not need to set a rigid threshold and apply it across all equipment. Instead, the AI learns what normal operation of a given piece of equipment is in order to then monitor for abnormalities. In other words, it is similar to learning what the appropriate threshold is for each and every piece of equipment. So, an AHU in Austin could in theory have a different alerting point than an AHU in Boston.
Based on this approach, ASHRAE 36 guidelines for AFDD can be implemented across the board with no concerns of whether your thresholds are appropriate or attainable.
Implementing AFDD has been proven to reduce maintenance costs and reduce energy consumption by ensuring higher levels of system performance. AI and Elipsa help make that more attainable and easier than ever before to start optimizing the operation of your critical equipment.
To learn more, contact us at info@eipsa.ai or visit https://www.elipsa.ai
