Development of fault detection and diagnosis schemes for industrial refrigeration systems - lessons learned

Thybo, Claus; Izadi‐Zamanabadi, Roozbeh

doi:10.1109/cca.2004.1387544

Cited by 7 publications

(10 citation statements)

References 5 publications

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“…Domain model based approaches to detect faults and identify the root-causes have also been suggested in the literature [11,14]. These approaches rely on the ability to develop a well-calibrated domain model that mimics the operations of a real world refrigeration system.…”

Section: Related Workmentioning

confidence: 99%

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Bugs in the Freezer

Srinivasan

Vasan

Sarangan

et al. 2015

Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems

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Refrigeration is a major component of supermarket energy consumption. Ensuring faultless operation of refrigeration systems is essential from both economic and sustainability perspectives. Present day industry practises of monitoring refrigeration systems to detect operational anomalies have several drawbacks: (i) Overdependence on human skills; (ii) Limited help in identifying the root-cause of the anomaly; and (iii) Presumption about high degree of instrumentation -which prevents their usage in supermarkets in developing economies. Existing approaches in literature to detect anomalies in refrigeration systems either are done in controlled laboratory settings or assume the availability of sensory information other than energy. In this paper, we present an approach to detect anomalous behavior in the operation of refrigeration systems by monitoring their energy signals alone. We test the performance of our approach using data collected from refrigeration systems across 25 stores of a real world supermarket chain. We find that using energy signal, we can not only detect anomalies but also narrow down the possible root-cause of the anomaly to a reduced set. Further, using energy signal along with data collected from other sensors (if available) allows us to reduce the false positive rate while identifying the root-cause of the anomaly.

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Section: Related Workmentioning

confidence: 99%

“…Existing models for detecting anomalies in refrigeration systems and diagnosing the root causes have mostly been done in controlled laboratory settings [3,6,7,[11][12][13][14]17]. These may not be applicable in real world scenarios since the operating conditions may be different from a controlled environment.…”

Section: Introductionmentioning

confidence: 99%

Bugs in the Freezer

Srinivasan

Vasan

Sarangan

et al. 2015

Proceedings of the 2015 ACM Sixth International Conference on Future Energy Systems

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“…Several AFDD methods used this as the main sensitive feature for AFDD (Fisera and Stluka 2012;Mavromatidis et al 2013;Srinivasan et al 2015). Others use control signals, system states, valve positions, and various measurement points from the air-side or refrigerant-side of the system (Porter et al 2008;Thybo and Izadi-Zamanabadi 2004;Yang et al 2011aYang et al , 2011b.…”

Section: Supermarket Equipment Afddmentioning

confidence: 99%

“…Recent supermarket AFDD developments address HVAC and refrigeration systems (Mavromatidis et al 2013;Srinivasan et al 2015;Thybo and Izadi-Zamanabadi 2004;Yang et al 2011aYang et al , 2011b, as well as the lighting system (Mavromatidis et al 2013). These references stem from both industry and academic research of lighting, HVAC, and refrigeration systems.…”

Section: Introductionmentioning

confidence: 99%

Automated fault detection and diagnosis methods for supermarket equipment (RP-1615)

Behfar

Yuill

2017

Science and Technology for the Built Environment

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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.

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“…Model based fault diagnosis scheme generally based upon two types of models (i) physical model [4][5][6] (ii) Black box model [7][8]10]. After literature review it has been found that model based fault diagnosis scheme using physical laws has used either refrigeration circuit model [5][6]11] or air circuit model [4,12] separately. The complete model of an AC system i.e.…”

Section: Introductionmentioning

confidence: 99%