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

Journal Article


Abstract


  • 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.

Publication Date


  • 2016

Citation


  • Yan, R., Ma, Z., Kokogiannakis, G. & Zhao, Y. (2016). A sensor fault detection strategy for air handling units using cluster analysis. Automation in Construction, 70 77-88.

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5907

Number Of Pages


  • 11

Start Page


  • 77

End Page


  • 88

Volume


  • 70

Abstract


  • 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.

Publication Date


  • 2016

Citation


  • Yan, R., Ma, Z., Kokogiannakis, G. & Zhao, Y. (2016). A sensor fault detection strategy for air handling units using cluster analysis. Automation in Construction, 70 77-88.

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers/5907

Number Of Pages


  • 11

Start Page


  • 77

End Page


  • 88

Volume


  • 70