An auto-deployed model-based fault detection and diagnosis approach for Air Handling Units using BIM and Modelica

Autor: Ando Andriamamonjy, Dirk Saelens, Ralf Klein
Rok vydání: 2018
Předmět:
Technology
Engineering
Civil
Modelica
Computer science
020209 energy
Model based method
COLLABORATIVE DESIGN
0211 other engineering and technologies
GENETIC ALGORITHM
02 engineering and technology
Fault detection and isolation
Damper
Engineering
SYSTEMS
Fault Detection and Diagnosis (FDD)
021105 building & construction
0202 electrical engineering
electronic engineering
information engineering
METHODOLOGIES
STRATEGY
OPTIMIZATION
Civil and Structural Engineering
computer.programming_language
Industry Foundation Class (IFC)
Science & Technology
BUILDING ENERGY
Estimation theory
business.industry
PROGNOSTICS
Building Information Model (BIM)
Building and Construction
Python (programming language)
Reliability engineering
Building information modeling
Control and Systems Engineering
Software deployment
SIMULATION
Construction & Building Technology
Prognostics
business
computer
PARAMETER-ESTIMATION
Zdroj: Automation in Construction. 96:508-526
ISSN: 0926-5805
DOI: 10.1016/j.autcon.2018.09.016
Popis: © 2018 Elsevier B.V. The Air Handling Unit (AHU) is one of the most energy consuming devices in building systems. Fault Detection and Diagnosis (FDD) methods integrated into AHUs can help to ensure that they comply with the intended design, and their efficiency is maintained throughout the entire operational stage of the building. Nonetheless, the implementation and deployment of FDDs at the operational stage require an extensive effort. Especially, FDD approaches that rely on first principle models (model-based FDD) need to be manually implemented, and the information necessary for this process is scattered between several exchange formats and files, thus making it time-consuming, error-prone and subject to modellers’ poor judgment. This study aims at facilitating and partially automating the implementation and deployment of model-based FDD. An automated tool-chain that combines a BIM (Building Information Model)-to-BEPS (Building Energy Performance Simulation) tool with a model-based FDD approach is developed. The contribution of this paper lies in the extension of an existing BIM to Modelica BEPS method with an automated calibration approach and a novel model-based FDD. These three elements are integrated in a framework (implemented using Python) to reduce experts’ involvement in FDD implementation and deployment. The developed model-based FDD combines a parity relation procedure for fault detection and profile identification for fault diagnosis. The latter uses the robust multi-objective optimisation algorithm NSGA-2. An error is detected when the difference between prediction and measured data over a specific time window is superior to a predefined threshold. The origin of the error is subsequently identified by estimating the profile of the different controllable components’ control signal. The developed tool-chain was applied to an actual AHU as well as on several numerical scenarios to identify typical AHU faults such as faulty dampers, valves and sensors. This study shows that the developed model-based FDD approach can identify some of the most common faults in AHUs, but more importantly that BIM can facilitate the deployment of model-based FDD in building systems. ispartof: Automation in Construction vol:96 pages:508-526 status: published
Databáze: OpenAIRE
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