Fault detection for buildings using uncertain parameters and interacting multiple-model method

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00335591" target="_blank" >RIV/68407700:21230/22:00335591 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-85318-1_77" target="_blank" >http://dx.doi.org/10.1007/978-3-030-85318-1_77</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-85318-1_77" target="_blank" >10.1007/978-3-030-85318-1_77</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fault detection for buildings using uncertain parameters and interacting multiple-model method

Popis výsledku v původním jazyce

Model-based fault detection and diagnosis (FDD) systems for buildings are very demanding on the solution set up eort. One rea- son is the requirement to use a high-delity model which must be created for each building separately. The FDD approach that reduces this bur- den is proposed in this paper. Proposed FDD algorithms are based on the interacting multiple-model (IMM) method and Kalman ltering for systems with uncertain parameters. The uncertain parameters in models enable to use "average" zone models rather than high-delity models, which simplies real applications. Detection of single and multiple faults is demonstrated on an example where faults, that might cause ine- ciency of control, are detected. Results show that the performance of the proposed algorithms with average zone models is comparable to the performance of the conventional IMM with accurate models.

Název v anglickém jazyce

Fault detection for buildings using uncertain parameters and interacting multiple-model method

Popis výsledku anglicky

Model-based fault detection and diagnosis (FDD) systems for buildings are very demanding on the solution set up eort. One rea- son is the requirement to use a high-delity model which must be created for each building separately. The FDD approach that reduces this bur- den is proposed in this paper. Proposed FDD algorithms are based on the interacting multiple-model (IMM) method and Kalman ltering for systems with uncertain parameters. The uncertain parameters in models enable to use "average" zone models rather than high-delity models, which simplies real applications. Detection of single and multiple faults is demonstrated on an example where faults, that might cause ine- ciency of control, are detected. Results show that the performance of the proposed algorithms with average zone models is comparable to the performance of the conventional IMM with accurate models.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA18-26278S" target="_blank" >GA18-26278S: Zahrnutí apriorní informace při identifikaci nelineárních systémů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Lecture Notes in Control and Information Sciences - Proceedings

ISBN

978-3-030-85317-4

ISSN

2522-5383

e-ISSN

2522-5391

Počet stran výsledku

20

Strana od-do

—

Název nakladatele

Springer

Místo vydání

Wien

Místo konání akce

Bologna

Datum konání akce

21. 11. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—