Fault Detection and Diagnostics: How To Find Energy-Wasting Problems
In an effort to save energy, reduce maintenance costs, and leverage investments in existing building management system, more and more facility managers are beginning to research and deploy fault detection and diagnostics (FDD) software platforms in their facilities.
The 411 on FDD
FDD platforms consist of three major components: a way of acquiring data, a set of algorithms for processing it, and a means of displaying the results. If that sounds similar to a building automation system, don’t be surprised: Most FDD systems are designed as add-ons to an existing building management system, though some standalone platforms do exist. The difference is that, while modern BAS collect and store vast volumes of data, creating actionable results from that data is a time-consuming and difficult task. As anyone who has spent hours poring over building trend charts trying to figure out why they always get a particular alarm at 2 a.m. can tell you, this is a task worth automating. But to really save time and money, FDD systems must accurately identify real problems and provide an easily digestible idea of how to resolve it, if not an outright diagnosis. Getting it right is no easy task.
As with many cutting-edge software products, not everyone means the same thing when they talk about FDD. Generalizations are difficult, as even a single vendor’s FDD product is often infinitely configurable. No two software platforms are identical. At worst, FDD software may be nothing more than a fancy way to create alarms and trend plots, albeit with much more functionality than is built into most BAS.
Trending features can include plotting multiple time series of overlapped data, which may help determine the causes and effects of a problem by plotting one BAS point as a function of another, or plotting a mathematical expression based on multiple BMS points. True FDD systems attempt to detect and diagnose equipment failures by applying a sophisticated rule-base — what a computer scientist would call an “expert system.” Such a rule-base contains both general knowledge (“there should be little to no flow in a chilled water loop if the chilled water pump is off”) and knowledge specific to the facility in which it is deployed (“the chilled water loop delta T should never be greater than 12 degrees F or less than 4 degrees F”). From this embodied knowledge the system can make sophisticated deductions. Some FDD platforms come with this rule-base predefined and uneditable. Others allow users to modify or add to it, though often with a steep learning curve.
When the right steps are taken, FDD can be a huge success, speeding the diagnosis of subtle problems that might otherwise have taken months or years to discover. As FDD becomes a more regular add-on or feature of BAS, and as the costs of hardware and data storage continue to decrease, FDD may see wider and deeper deployment. Operator training and trust, and the ability of FDD software to provide targeted information with explanatory context, will continue to determine the success or failure of future FDD systems.
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