A sensor fault detection strategy for air handling units using cluster analysis

Sensors are an essential component in the control systems of air handling units (AHUs). A biased sensor reading

could result in inappropriate control and thereby increased energy consumption or unsatisfied indoor thermal

comfort. This paper presents an unsupervised learning based strategy using cluster analysis for AHU sensor

fault detection. The historical data recorded from sensors is first pre-processed to reduce the dimensions using

principal component analysis (PCA). The clustering algorithmOrdering Points to Identify the Clustering Structure

(OPTICS) is then employed to identify the spatial separated data groups (i.e. clusters),which possibly indicate the

occurrence of sensor faults. The data points in different clusters are then checked for temporal separation in order

to confirm the occurrence of sensor faults. The proposed sensor fault detection strategy is tested and evaluated

with the data collected from a simulation system. The results showed that this strategy can detect single and

non-simultaneously occurred multiple sensor faults in AHUs. The fault detection results were not strongly

affected by the selection of the user defined input parameters required in OPTICS.