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The Information Model That Powers Fault Detection and Diagnostics (FDD)

The most critical component of Fault Detection and Diagnostic (FDD) software is its Information Model. This Information Model acts as the “brain” that analyzes building data and produces the diagnostics that help building owners derive energy savings, operational savings, and better performing equipment. But not all Information Models are equal.

Here's what you need to know.
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Powering FDD

When considering the use of Fault Detection and Diagnostics (FDD) software, be sure to consider the ways your chosen software uses its Information Model to provide results.

In our latest whitepaper, read about the inherent weaknesses of FDD (messy data!), how to
reduce custom rules and false positives, and the five key elements of an information model that are required to adapt to complex building systems and provide accurate root-cause diagnostics.
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Written by Experts

Our experts cite the issues with many Information Models and how that can complicate your use of FDD.
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Filled With Actionable Tips

View specific examples of equipment data, analysis, and outputs that you should consider when selecting a FDD solution.
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Information Model Deep-Dive

Learn about the 5 characteristics of our Information Model that make the Clockworks platform unique.

About the Authors

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Jaime Gagne
Clockworks Analytics

Jaime Gagne, PhD is Vice President of Analytics at Clockworks Analytics. She is a lead architect of Clockworks’ extensive building ontology and library of fault detection diagnostics.
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Dennis Krieger
Clockworks Analytics

As Director of Partner Success at Clockworks Analytics, Dennis supports
facility service companies using building analytics software to maintain portfolios at peak performance.

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sales@clockworksanalytics.com | (857) 598-6439