Automatic Thresholding for Data Gap Detection for a Set of Sensors in Instrumented Buildings

Building systems are highly vulnerable to different
kinds of faults and failures. In fact, various faults, failures and human
behaviors could affect the building performance. This paper tackles
the detection of unreliable sensors in buildings. Different literature
surveys on diagnosis techniques for sensor grids in buildings have
been published but all of them treat only bias and outliers. Occurences
of data gaps have also not been given an adequate span of attention
in the academia. The proposed methodology comprises the automatic thresholding
for data gap detection for a set of heterogeneous sensors in
instrumented buildings. Sensor measurements are considered to be
regular time series. However, in reality, sensor values are not
uniformly sampled. So, the issue to solve is from which delay each
sensor become faulty? The use of time series is required for detection of abnormalities on
the delays. The efficiency of the method is evaluated on measurements
obtained from a real power plant: an office at Grenoble Institute of
technology equipped by 30 sensors.

Authors:

• Houda Najeh
• Stéphane Ploix
• Mahendra Pratap Singh
• Karim Chabir
• Mohamed Naceur Abdelkrim

Keywords:

• Building system
• time series
• diagnosis
• outliers
• delay
• data gap.

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