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While there are several software tools and applications that have proven beneficial for building operations and energy management, the software application with the best verified results and cost effectiveness is fault detection and diagnostics. It’s the killer app of the building automation and building energy management industry.
Fault detection and diagnostics are a subset of the larger category of analytic software related to buildings. Analytics are critical because buildings are becoming more complex, new systems are being introduced into buildings, and energy consumption metrics and key performance indicators are now of great interest to corporate or organization executives.
In general, analytic software tools primarily support technicians and engineers in the field who are dealing with both the everyday issues of building operations as well broader issues of complicated systems, advanced technology and higher expectations for building performance. The analytic tools provide insights into building systems resulting in reduced energy consumption, improved building performance and lower costs.
Fault detection and diagnostics for HVAC systems are not new. Research, development and testing of fault detection approaches have been around for about 20 years or so. What is new is the increased interest in and actual use of fault detection. As an example of industry approval, in October 2011, the U.S. Green Building Council and SCI Energy announced a technology agreement where building owners would be able to use SCI’s SCIwatch technology through the LEED Online platform. This analytic tool utilizes automated fault detection for ongoing commissioning and predictive maintenance in commercial buildings, something of extreme interest to USGBC and energy management in general.
Another example involving actual deployment and use of fault detection, and probably one of the most recent and best examples, was a pilot program at Microsoft’s Redmond campus. Microsoft installed a fault detection application that could “monetize” each fault and identify the annual cost of the fault. Not only did Microsoft discover faults they were never aware of, but their engineers saved significant time in addressing operational issues. This tool allows Microsoft’s typical five-year retro-commissioning cycle for their campus to be accomplished in just one year. Annual energy cost savings for Microsoft from automated fault detection alone may exceed $1 million. (To read the Microsoft case study, click here.)
Lawrence Berkeley Laboratories in a study on monitoring-based commissioning, an element of which is building diagnostics, showed an average energy savings of 10 percent, with as much as 25 percent in some cases. When you have organizations such as USGBC, Microsoft and Lawrence Berkley Labs broadly supporting the results and benefits of fault detection, there’s something to its application.
System faults are different than traditional system alarms. Faults deal with a system’s performance; that is, the system is operating but performing sub-optimally and the software application identifies the reason for the sub-optimal performance or fault.
The “fault detection and diagnostics” tools in the marketplace may use different methods to detect a fault and have different capabilities for diagnosis. The major differences with the FDD approaches involve capabilities to identify single or multiple faults, the type of building systems to be monitored, whether diagnosis is provided by the tool or done manually, as well as conveying to the building operator the consequences of the fault, such as monetizing the fault.