Automatic Detection and Diagnosis of faults in Sensors used in EMS

Conference paper (2016)

Authors

A. Taal The Hague University of Applied Sciences

L.C.M. Itard The Hague University of Applied Sciences, OLD Housing Quality and Process Innovation

W. Zeiler Eindhoven University of Technology

Y. Zhao Eindhoven University of Technology

Research Group

OLD Housing Quality and Process Innovation

Fault diagnosis Fault detection FDD Systems theory Bayesian method Sensor faults BBN Building Energy Management System Energy Monitoring System Model faults, HVAC equipment

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http://resolver.tudelft.nl/uuid:ca56d9a0-c3e5-439a-a6e6-4920284f0a39

Published Date

2016

Language

English

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Research Group

OLD Housing Quality and Process Innovation

Abstract

A much occurring problem in the Energy Management Systems of existing buildings and HVAC services is that the measurements are unreliable. In this article a methodology is described which can be used to determine the presence of errors in energy monitoring, caused by faulty measurements. These errors can be detected and subsequently diagnosed. Detection of monitoring errors is done based on occurring symptoms. Determination of these symptoms is done using the laws of conservation of energy, mass and pressure. The diagnosis is done by using a statistical method based on Bayesian theory in which the chance of an error occurring is determined based on ( combinations of) the symptoms. The method is built in a Bayesian Belief Network (BBN) software tool. The advantage of BBN is that it is consistent with the working methods of experts in installation technology.

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