Generator for modular virtual sensors

Hensel, Burkhard; Kabitzsch, Klaus

doi:10.1109/etfa.2016.7733726

Cited by 6 publications

(9 citation statements)

References 9 publications

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“…In complex systems, it is not trivial to design effective virtual sensors, due to the large combination of available inputs but also to the diversity of modeling techniques. While a popular approach is to use general purpose simulation software, there is research effort to produce software tools able to create and parametrize modular virtual sensors [ 19 ].…”

Section: State Of the Artmentioning

confidence: 99%

A Method for Fault Detection and Diagnostics in Ventilation Units Using Virtual Sensors

Mattera

Quevedo

Escobet

et al. 2018

Sensors

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Buildings represent a significant portion of global energy consumption. Ventilation units are complex components, often customized for the specific building, responsible for a large part of energy consumption. Their faults impact buildings’ energy efficiency and occupancy comfort. In order to ensure their correct operation, proper fault detection and diagnostics methods must be applied. Hardware redundancy, an effective approach to detect faults, leads to increased costs and space requirements. We propose exploiting physical relations inside ventilation units to create virtual sensors from other sensors’ readings, introducing redundancy in the system. We use two different measures to detect when a virtual sensor deviates from the physical one: coefficient of determination for linear models, and acceptable range. We tested our method on a real building at the University of Southern Denmark, developing three virtual sensors: temperature, airflow, and fan speed. We employed linear regression models, statistical models, and non-linear regression models. All models detected an anomalous strong oscillation in the temperature sensors. Readings fell outside the acceptable range and the coefficient of determination dropped. Our method showed promising results by introducing redundancy in the system, which can benefit several applications, such as fault detection and diagnostics and fault-tolerant control. Future work will be necessary to discover thresholds and set up automatic fault detection and diagnostics.

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Section: State Of the Artmentioning

confidence: 99%

A Method for Fault Detection and Diagnostics in Ventilation Units Using Virtual Sensors

Mattera

Quevedo

Escobet

et al. 2018

Sensors

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“…A first description of the developed software has been given in [1]. The coordinator manages a modular model where the subsystems are connected via "signals," as scientifically known from "systems theory."…”

Section: Software Overviewmentioning

confidence: 99%

“…Instead, it sets a coordination view on top of them [1]. Besides process identification, this software has its focus on virtual sensor design and can thus be used not only on standard PCs in offline mode, but also on embedded PCs in online mode.…”

Section: Introductionmentioning

confidence: 99%

“…Besides process identification, this software has its focus on virtual sensor design and can thus be used not only on standard PCs in offline mode, but also on embedded PCs in online mode. In the state at the publication of [1] it was not possible to use external models in that software. The software has now been extended to support different external model types 2 Journal of Computational Engineering (e.g., Matlab and Python scripts) additionally to "internal" model types (strictly speaking realized as Java plug-ins) like static equations, difference equations, or discrete time and continuous-time transfer functions.…”

Section: Introductionmentioning

confidence: 99%

“…There are a lot of tools which can be used for parameter identification; an overview is given in [1]. Probably the most frequently used software is Matlab [2].…”

Section: Introductionmentioning

confidence: 99%

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New Coordination Software for Parameter Identification Applied to Thermal Models of an Actuator Strut

Hensel

Schroeder

Kabitzsch

2017

Journal of Computational Engineering

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The practical worth of models of technical processes depends on their accuracy, that is, the difference between model outputs and real measurements. For minimizing these differences, process identification methods are used. In this article, coordination software for process identification is presented which has the unique feature that it allows the integration of models that have been created with external tools, for example, Matlab or Python scripts. There is no need to transform the models into another type of software format to use the common identification coordinator. The concept of the software is described and two examples for the coupling with external simulation software are given. Additionally, this article contains a detailed case study of the parameter identification of two models using that identification coordination software. This highlights the benefit of the new coordination software regarding similar work flow for different model types. The modeled physical subject is the thermal behavior of an actuator strut.

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