English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

The fault detection by stochastic qualitative reasoning is an effective way for complex systems such as a building air conditioning system. In this framework, the fault part of a system can be identified by comparing the behavior derived by stochastic qualitative reasoning with the measured behavior. The measured behavior is represented as a series of qualitative values that are obtained by classifying quantitative measurements into several qualitative categories based on the definition of the qualitative regions. The fault detection often fails under the inappropriate definition. This paper proposes a method for defining the normal qualitative regions from the field data of a building air conditioning system. Measurement data in the normal condition must be converted into the stable qualitative value so that the behavior can be distinguished from fault conditions. Therefore, by the probability of occurrence of qualitative value in the reasoning behavior, qualitative regions are determined. This method is applied to a real building air conditioning system. According to the definition of qualitative regions determined from the field data, the fault parts can be successfully identified.

The Definition of Qualitative Regions for Fault Detection of a Building Air Conditioning System by Stochastic Qualitative Reasoning