Framework for Knowledge-Based Fault Detection and Diagnostics in Multi-Domain Systems: Application to HVAC Systems
Framework for Knowledge-Based Fault Detection and Diagnostics in Multi-Domain Systems: Application to HVAC Systems
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Date
2017-11-13
Authors
Delgoshaei, Parastoo
Austin, Mark
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Abstract
State-of-the-art fault detection methods are equipment and domain specific and non-comprehensive.
As a result, the applicability of these methods in different domains is
very limited and they can achieve significant levels of
performance by having knowledge of the domain and the
ability to mimic human thinking in identifying the
source of a fault with a comprehensive knowledge of the system and its surroundings.
This technical report presents a comprehensive semantic framework for
fault detection and diagnostics (FDD) in systems simulation and control.
Our proposed methodology entails of implementation of the
knowledge bases for FDD purposes through the utilization of
ontologies and offers improved functionalities of such
system through inference-based reasoning to
derive knowledge about the irregularities in the operation.
We exercise the proposed approach by working step by step through
the setup and solution of a fault detection and diagnostics problem
for a small-scale heating, ventilating and air-conditioning (HVAC) system.