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Abstract
Many automated fault detection and diagnostics methods have been developed for application to building mechanical systems over the past 20 years because they have the potential to reduce operating costs and energy consumption by providing early warning of performance degradation faults. Supermarkets could be a very beneficial setting to deploy automated fault detection and diagnostics, particularly in the refrigeration systems, which are major energy users and are known to commonly suffer from significant refrigerant leakage problems. The current article provides an overview of the common mechanical systems deployed in supermarkets, and then describes a comprehensive review of the literature on automated fault detection and diagnostics methods from other systems that could potentially be applied in supermarket settings. A collection of supermarket field data is analyzed in the context of its potential use in automated fault detection and diagnostics methods from other systems. The review includes methods to categorize and assess the automated fault detection and diagnostics approaches, from the perspective of a potential adopter of automated fault detection and diagnostics technology for a supermarket setting. The article concludes that supermarket automated fault detection and diagnostics is still in the early stages of development and that there is a need to further develop automated fault detection and diagnostics methods for supermarket applications. To facilitate the development of supermarket-specific automated fault detection and diagnostics approaches, additional data sets from refrigeration equipment are needed.
Nomenclature
| A | = | = area |
| C | = | = heat capacity |
| E | = | = energy consumption |
| EKF | = | = extended Kalman filter |
| h | = | = enthalpy |
| J | = | = residual evaluation function |
| KF | = | = Kalman filter |
| LH | = | = latent heat |
| = | = mass flow rate | |
| M | = | = mass |
| P | = | = pressure |
| = | = heat transfer rate | |
| r | = | = residual |
| SH | = | = sensible heat |
| t | = | = time |
| T | = | = temperature |
| U | = | = heat transfer coefficient |
| UA | = | = heat transfer conductance |
Greek
| ϵ | = | = error |
Subscripts
| air | = | = air |
| A | = | = actual |
| airload | = | = airload |
| amb | = | = ambient |
| c | = | = condensing |
| cs | = | = condenser split |
| discharge | = | = discharge |
| drop_leg | = | = drop-leg pipe that connects the condenser to the receiver |
| e | = | = evaporating |
| goods | = | = goods |
| i | = | = inlet |
| = | = mass flow rate | |
| o | = | = outlet |
| P | = | = predicted |
| r | = | = refrigerant |
| ra | = | = return air |
| sat | = | = saturation |
| sc | = | = subcooling |
| wall | = | = wall |
Funding
The research work is supported by ASHRAE 1615 RP, sponsored by TC 7.5 Smart Building Systems, and TC 10.07 Commercial Food and Beverage Refrigeration Equipment. The authors are thankful to ASHRAE for funding this project.