Data driven investigation of faults in HVAC systems with model, cluster and compare (MCC)

Balakrishnan, N.; Gupta, Rajesh K.; Agarwal, Yuvraj

doi:10.1145/2674061.2674067

Cited by 52 publications

(35 citation statements)

References 18 publications

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“…e.g., multiple rooms could be divided by room type, but also by room location, such as by floor number or building side, or by other characteristics. When a room shows anomalous behaviour within its group, it could be due to faulty components, but also to incorrect or insufficient grouping, as shown by Narayanaswamy et al in Reference [9]. e.g., the only classroom on the top floor might deviate from all other classrooms, which are on the ground floor, due to different thermal loss.…”

Section: Consensus-based Methods For Anomaly Detectionmentioning

confidence: 99%

“…To the best of our knowledge, no previous work has been done on using consensus-based techniques for FDD in buildings systems, and specifically on VAV units used to control CO 2 level. Narayanaswamy et al present a model, cluster and compare method for FDD on VAV units, where data from several units are used to detect anomalies [9]. Linear models are trained for each individual VAV unit, and the obtained parameters undergo a clustering procedure.…”

Section: State-of-the-artmentioning

confidence: 99%

“…Their faults, such as incorrect HVAC on/off modes or inappropriate setpoints for thermostats, are responsible for a large share of energy waste [7]. While many FDD techniques have been applied to the large air handling unit (AHU), faults and misconfigurations involving variable air volume (VAV) units at room level are often ignored [9]. However, considering that the VAV units have the main responsibility of direct air supply to each room, and taking into account the importance of attaining good indoor air quality and thermal comfort, proper monitoring and FDD investigations of VAV units seems very sensible.…”

Section: Introductionmentioning

confidence: 99%

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Consensus-Based Method for Anomaly Detection in VAV Units

2019

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Buildings account for large part of global energy consumption. Besides energy consumed due to normal operation, a large amount of energy can be wasted due to faults in buildings subsystems. Fault detection and diagnostics techniques aim to identify faults and prevent energy waste, but are often difficult to apply in practice. Data-driven methods, in particular, require an adequate amount of fault-free training data, which is rarely available. In this paper, we propose a method for anomaly detection that exploits consensus among multiple identical components. Even if some of the components are faulty, their aggregate behaviour is overall correct, and it can be used to train a data-driven model. We test our method on variable-air-volume units in an existing building, executing two experiments grouping the components according to ventilation unit, and according to room type. The two experiments identified the same set of anomalous components, i.e., their behaviour was different from the rest of the group in both cases, and this suggests that the anomaly was not due to wrong group assignment. The proposed method shows the potential of exploiting consensus among multiple identical systems to detect anomalous ones.

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Section: Consensus-based Methods For Anomaly Detectionmentioning

confidence: 99%

Section: State-of-the-artmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Consensus-Based Method for Anomaly Detection in VAV Units

2019

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“…al. [9] adopt a data-driven approach to detect faults in HVAC usage. Their focus is on detecting faults in variable air volume (VAV) control settings and use parameters which are very HVAC specific.…”

Section: Related Workmentioning

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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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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“…There are some publications with the intention to detect malfunctions in HVAC systems, such as Ref. [12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Micro-Organism Growth Risk on Filters with Machine Learning

Hantsch¹,

Döge

2019

E3S Web Conf.

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Modern buildings usually have a practically air-tight envelope. Therefore, mechanical ventilation is very often necessary. A crucial part of the system is the filter which allows to create an atmosphere which is free of dust, aerosols, and pollen. As organic material accumulates on the filter surface, the risk of micro-organism growth rises. This may yield health issues especially for the occupants of buildings in humid regions. For this purpose, a test filter with electrodes has been designed which allowed to measure its electro-magnetic properties, such as resistance, capacitance, and impedance as an indicator for the micro-organism growth risk. After some preliminary tests, electrodes of stainless steel and the electrical capacitance have been selected due to their best durability and signal-to-noise-ratio. The test filter has been implemented in the HVAC system of the institute in order to aggregate data for different abnormal and normal operation data. A machine learning algorithm has been trained successfully to detect anomalies of the filter behaviour and therefore provided more insight than pressure drop measurement alone. Finally, the change intervals of the filter could be adapted to the real degree of pollution without the requirement for visual observation in order to provide best air conditions.

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Data driven investigation of faults in HVAC systems with model, cluster and compare (MCC)

Cited by 52 publications

References 18 publications

Consensus-Based Method for Anomaly Detection in VAV Units

Consensus-Based Method for Anomaly Detection in VAV Units

Bugs in the Freezer

Assessment of Micro-Organism Growth Risk on Filters with Machine Learning

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