Issues and Concerns in Implementing FDD

Lack of Data: FDD needs data from the BAS systems. If there are not enough sensors, the sensors are inaccurate, or the building has a legacy control system, there can be issues with obtaining the data required.

Rules Specific to Building Systems: The rules apply to specific HVAC relationships and equipment, and building owners need to be assured that their specific building systems are addressed by the FDD software application or can be developed. Many FDD software products start with FDD rules developed by NIST that are then augmented with rules developed by others or by the companies themselves.

How to Handle the FDD Information: Facility Management organizations need to decide how best to handle the FDD information. A fault indicates that the system may be operational, but, is not performing optimally. Faults should not be treated as an alarm. An alarm is a condition meaning immediate response. Faults need to be addressed. Some facility management groups set faults aside, study the remedies, and manually prioritize the faults. Monetizing the faults helps in prioritizing the faults.

Using the Diagnostic Data: Many FDD-based software tools can provide information to the technician or engineer regarding potential corrective actions. This information should be integrated into the work order system, which may be one application in a whole suite of facility management applications, in order to effectively utilize the information.

Prognostics Data: While FDD seems inherently capable of providing prognostic data (which it can analyze for fault conditions or degradation faults, predicting when a component will fail) very little has been developed in the area of predictive maintenance. In addition, prognostic data would allow for more proactive condition-based maintenance which may be a better approach for facility management organizations that are reactive and corrective.

Figure 7.1

Lack of Applications For Emerging Systems: FDD routines do not currently address newer on-site energy sources such as solar, wind or geothermal, or touch on power management or demand response.

SaaS and Hosted Systems: Many of the current offerings are provided as Software as a Service (SaaS). This can be an issue with corporate IT departments because of the need to pierce the IT firewalls and security. However, some facility management organizations see it as an advantage because it means less involvement and dependence on the IT department.

Alternative Ways to Deploy FDD: At some point in the future control manufacturers will integrate FDD routines into their controllers, starting with the large equipment such as chillers.

Keeping a significant energy consuming system such as the HVAC running at optimal performance is challenging. Many times failures or suboptimal performance goes unnoticed for long periods of time. Case studies from companies vending FDD-based software services can show energy savings in the 10% to 35% range with the capability to correctly identify faults and the primary response 95% of the time.

Software based on FDD is a new class of tools for building owners adding some "smarts” to a smart building. It's not difficult to imagine similar tools for other building systems and the potential for enhanced intelligence built into tools for facility management.Recently, the best use of an analytic software application for building systems has been fault detection and diagnostics (FDD) for HVAC systems. There is research including case studies with verified results showing that analytic software reduced energy consumption, improved the efficiency and effectiveness of building operation, and reduced building operation costs. Once used, FDD becomes a core operational tool for many facility management organizations.

Despite the impressive progress with FDD, the industry is in its infancy in deploying data analytic applications in buildings. If analytics for the HVAC system have provided outstanding outcomes, we need to take that template to other building systems. Such applications are based on rules

of how the system should optimally operate, generally obtained from the original design documents, and monitoring key data points in near realtime

Figure 7.2 of how the system should optimally operate, generally obtained from the original design documents, and monitoring key data points in near realtime. You essentially compare the real-time data with the rules, and if the data adheres to the rule, the system is fine. If not, the system is not running optimally and has a fault. For those systems that are not process based, applying analytics generally uses statistical monitoring of key performance indicators (KPIs) to monitor outliers. This may not provide diagnosis of an issue, but it can identify faulty equipment for preventative maintenance.

 
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