A fault detection system based on two complementary methods and continuous updates

Benndorf, Gesa A.; Wystrcil, Dominik; Réhault, Nicolas

doi:10.1016/j.ifacol.2018.09.601

Cited by 7 publications

(4 citation statements)

References 4 publications

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“…Recursive weighting can also be used to make well-known fault detection methods such as principal component analysis adaptive, as in [9]. Further, it is also possible to update the system with information about faults [10]. The selection of data before updating the model represents a special approach for such an adaptive procedure.…”

Section: Related Results In the Literaturementioning

confidence: 99%

Adaptive Methods for Fault Detection on Research Engine Test Beds

Schadler¹,

Wohlthan²,

Wimmer³

2023

Proceedings of the 18th IMEKO TC10 Conference on Measurement for Diagnostics, Optimisation and Control

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In the case of test beds for research engines, fault detection methods that use models based on historical data face a particular challenge. Due to the experimental design of the test bed, offline training of statistical models with a data set containing all possible variations is simply not possible. The methods must adapt to the current data situation directly on-site. But this involves risks. First, computational time and memory requirements can become extremely large with high data volumes. Second, the data may be faulty and thus negatively affecting the models. To avoid both, a selection of data is made before it is used to build the fault-free reference model. For this purpose, a new statistic is presented as the combination of the Mahalanobis distance and the forecast residual. With it, it is possible to reduce the update frequency and to increase the rate of detected faulty points, since the models are no longer manipulated by faulty data points and thus the residuals provide a better structure for fault detection.

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Section: Related Results In the Literaturementioning

confidence: 99%

Adaptive Methods for Fault Detection on Research Engine Test Beds

Schadler¹,

Wohlthan²,

Wimmer³

2023

Proceedings of the 18th IMEKO TC10 Conference on Measurement for Diagnostics, Optimisation and Control

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“…Further improvements in the application of the methodology are possible by extending the measurement hardware prototypes with additional sensors and alternate wireless protocols. Finally, COMETH Rules is one part of the COMETH toolset which also includes data driven routines based on Machine Learning [11]. Further investigations on the application of such methods in the described context are being carried out.…”

Section: Discussionmentioning

confidence: 99%

A mobile measurement solution for fault detection and diagnosis in buildings

Rist

Ihlenburg

Réhault

2022

IOP Conf. Ser.: Earth Environ. Sci.

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Buildings are equipped with increasingly complex Heating, Ventilation and Air Conditioning (HVAC) systems whose energy efficiency can degrade over time because of unnoticed faults. Facility managers (FM) often do not have the time and adapted tools to identify and correct issues. Building Automation Systems generate a multitude of measurement data that can be used to identify operational malfunctions at an early stage by applying fault detection and diagnosis (FDD) methods. However, this data itself can be hardly accessible and contain errors for example due to the degradation of the sensor quality over time. These issues limit the application of analytic methods like FDD in existing buildings. In this paper, we present a prototype implementation of a mobile measurement system based on Internet of Things (IoT) technologies that functions together with an automated FDD system to detect operational faults in HVAC systems independently of the existing measurement infrastructure. Additionally, the solution provides a user interface giving a quick overview on found faults and displaying them in spatial relation with the HVAC system through an integrated BIM visualization. Here, we describe the technical details and the required conditions for the use of our prototype and show the results of an exemplary implementation on an air handling unit.

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“…70,72 A relatively new topic in the field is the development of adaptive methods, which adjust to changing conditions and learn from user feedback. 118,217 The user, e.g., the facility manager, can hereby validate and improve an existing FDD method during the course of its application by identifying misclassified data. Such methods can alleviate the wellknown difficulties with parametrization of black-box models, 102 labeling of training data, and high false positive rates.…”

Section: Discussionmentioning

confidence: 99%

Energy performance optimization in buildings: A review on semantic interoperability, fault detection, and predictive control

Benndorf

Wystrcil

Réhault

2018

Applied Physics Reviews

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The building sector accounts for about 30% of the global final energy consumption. Most of the consumed energy originates from fossil fuels. The operation of buildings is known to suffer from various deficiencies, degrading their energy performance. An untapped potential lies, therefore, in the optimization of building operation to significantly reduce CO 2 emissions and to increase the cost effectiveness and user comfort. Over the past 40 years, extensive research has been carried out to investigate and develop methods for building performance optimization based on measured data from building services, such as heating, ventilation, air conditioning, and lighting systems. The ongoing digitalization trend in the building sector offers the opportunity to easily access large amounts of high-quality measurement data and semantic building information as digital descriptions. This facilitates the development and implementation of automated routines for the continuous supervision and optimization of building operation, including reliable fault detection and diagnosis and model-predictive control. This review article is focused on three major research topics in the field of energy-efficient buildings, namely, semantic interoperability between heterogeneous and complex systems, methods for fault detection and diagnosis, and model-predictive control. V

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A fault detection system based on two complementary methods and continuous updates

Cited by 7 publications

References 4 publications

Adaptive Methods for Fault Detection on Research Engine Test Beds

Adaptive Methods for Fault Detection on Research Engine Test Beds

A mobile measurement solution for fault detection and diagnosis in buildings

Energy performance optimization in buildings: A review on semantic interoperability, fault detection, and predictive control

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