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Non-linear recursive parameter estimation applied to fault detection and diagnosis in real buildings

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conference contribution
posted on 2012-07-26, 11:06 authored by Richard BuswellRichard Buswell, Philip Haves, Timothy I. Salsbury, Jonathan WrightJonathan Wright
This paper describes a fault detection and diagnosis scheme based on parameter estimation and presents results from its application to a cooling coil subsystem in a real building. A non-linear adaptation of the Prediction Error Forgetting (PEF), algorithm is employed to estimate the parameters of a simple, steady-state, first principles based cooling coil model. A steady-state detector is used to discard data with excessive transients. Three model parameters represent possible faults; control valve leakage, coil fouling and sensor offset. These parameters are estimated recursively, together with the uncertainty in the estimated values. A significant change in a particular parameter indicates abnormal operation and suggests a diagnosis. The paper describes the first-principle models and their fault parameters, the steady-state detector, and the recursive parameter estimation algorithm. Results from the application of the technique to data measured in a test building demonstrate that valve leakage and coil fouling can be detected and diagnosed. The applicability of the approach to fault detection and diagnosis in real systems is also discussed.

History

School

  • Architecture, Building and Civil Engineering

Citation

BUSWELL, R.A., HAVES, P., SALSBURY, T.I. and WRIGHT, J.A., 2002. Non-linear recursive parameter estimation applied to fault detection and diagnosis in real buildings. IN: Proceedings of the 6th International Conference on System Simulation in Buildings, University of Liege, Belgium, 15-18 December 2002.

Version

  • AM (Accepted Manuscript)

Publication date

2002

Notes

This is a conference paper.

Language

  • en