Develop fault detection and diagnostic (FDD) methods to ensure air conditioners (ACs) and heat pumps (HPs) perform as designed throughout their lifetime and develop a testing and rating methodology to assess relative merits of different commercial FDD products.
Fault detection and diagnostic (FDD) methods are receiving increasing consideration for application in space-conditioning equipment as a method to reduce energy consumption and refrigerant emissions, and to provide more reliable comfort. It is anticipated that utility rebate programs and building energy regulations will promote the use of FDD methods as cost-effective energy efficiency measures leading to increased market acceptance of FDD methods. This project will accelerate market penetration of air conditioner and heat pump FDD technology by developing effective FDD algorithms and by formulating a standard procedure for rating different commercial FDD products based on their potential to avoid performance degradation and increased energy consumption.
This project will advance measurement science and facilitate implementation of residential FDD methods by developing new adaptable FDD algorithms to detect AC and HP faulty operation. Adaptable FDD methods are required to accommodate different equipment installations and aging effects. These FDD methods will not only be applicable to residential heat pumps, but also to other systems that operate on the vapor-compression principle. Generalization of these techniques and application of the statistical methods engrained within the FDD algorithms will give U.S. industry opportunities for innovation and will promote faster introduction of this technology into the marketplace.
FDD devices, like any other products marketed based on functionality, must have their performance metrics determined to promote market competition and product improvement. Two performance metrics will be developed for FDD modules: the first, "energetic" metric will capture potential energy savings as a result of detecting/correcting various faults, and the second metric will capture the FDD device's ability to avoid false alarms.
The main product of the project will be an AC/HP Tester/Evaluator. During a test of a commercial FDD method (e.g., an algorithm embedded in a heat pump's control unit), the AC/HP Tester/Evaluator will output a set of heat pump parameters that potentially could be used by the FDD module and, based on these parameters, the module will make a diagnosis regarding the "health" of the heat pump system. Calculation of the "energetic" metric will include weather data to weight the robustness of the FDD device's diagnostic capabilities over an entire year.
Three complementary tasks have been defined to achieve the goals of this project. The goal of Task 1 is to develop and refine an adaptable (self-training) FDD algorithm suitable for deployment in field-assembled systems. The FY2014 and FY2015 effort focused on devising a novel self-training methodology for developing no-fault performance models of a split heat pump operating in the cooling mode. This methodology was devised using steady-state laboratory measurements taken in previous years; however, its validation and refinement requires "real world" data that includes system cycling and noisy transients. These additional cooling mode data were collected on a low-efficiency heat pump in the cooling mode in FY2015 to facilitate the validation and refinement effort in FY2016.
Looking forward, the project envisions monitoring of field installed heat pumps to enable further refinement of FDD methods. With this goal in mind, we formulated and monitor an SBIR project to develop a prototype custom data collection module for remotely monitoring and performing FDD on residential split AC&HP systems. We expect Phase II of the SBIR project to be completed in FY2017.
The goal of Task 2 is to develop a test method for rating AC and HP FDD protocols. The significant elements of this complex task are: (1) development of data needed for understanding how different system configurations respond to common faults, (2) development of "game-proof" testing scenarios and (3) development of a performance metrics for FDD protocols. The method for evaluating general FDD algorithms employed in hand-held devices was completed in FY2015. The products of this phase of the project include a Tester/Evaluator Module, which resides at a publicly available website, and a methodology for determining a relative figure of merit of different FDD algorithms. The differences in responses to operating faults point to the inherent challenge of developing a universal FDD algorithm as well as to the difficulty of developing a general methodology for rating different FDD methods.
Starting in FY2015 and going into FY2016, the project initiated an extensive laboratory test program to study in more detail responses of different systems to common single and multiple simultaneous faults. Following the completion of tests on a low-efficiency unit, a high-efficiency heat pump will be tested according to the same test matrix. The data collected on different systems will be used in developing a protocol for evaluating embedded FDD algorithms. In FY2016 we will also analyze the merits of using the inverse modelling technique (used in Tester/Evaluator Module) versus data correlations to predict no-fault and faulty system parameters and will explore different concepts for testing FDD methods embedded in specific ACs and HPs.
In January of 2012, ASHRAE Standard Project Committee 207P "Laboratory Method of Test of Fault Detection and Diagnostics Applied to Commercial Air-Cooled Packaged Systems" was formed to promote FDD. This committee has directly used concepts and software developed in Task 2 to formulate test methods for different types of faults. NIST personnel have actively supported the work of this committee by chairing a sub-committee on airflow faults and contributing to the development of standardized definitions of faults. It is anticipated that a draft standard will be released for public comments in FY2016.
The goal of Task 3 is to ensure proper operation of the high-efficiency, air-source heat pump selected for the EL Net-Zero Energy Residential Test Facility (NZERTF). This heat pump is the top energy user in the home and must operate fault free for the home to reach its goal of net-zero energy. In FY2016 the NZERTF heat pump performance data taken over the entire cooling season will be used to debug and refine the adaptive FDD algorithm (A-FDD) developed under Task 1 with the goal of implementing A-FDD once it has been refined.
Start Date:October 1, 2011
Lead Organizational Unit:el
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