Evaluation of the suitability of different chiller performance models for on-line training applied to automated fault detection and diagnosis (RP-1139)

This paper presents the research results of comparing the suitability of four different chiller performance models to be used for on-line automated fault detection and diagnosis (FDD) of vapor-compression chillers. The models were limited to steady-state performance and included (a) black-box multivariate polynomial (MP) models; (b) artificial neural network (ANN) models, specifically radial basis function (RBF) and multilayer perceptron (MLP); (c) the generic physical component (PC) model approach; and (d) the lumped physical Gordon-Ng (GN) model. All models except for (b) are linear in the parameters. A review of the engineering literature identified the three following on-line training schemes as suitable for evaluation: ordinary recursive least squares (ORLS) under incremental window scheme, sliding window scheme, and weighted recursive least squares (WRLS) scheme, where more weight is given to newer data. The evaluation was done based on five months of data from a 220 ton field-operated chiller from Toronto (a data set of 810 data points) and fourteen days of data from a 450 ton field-operated chiller (a set of about 1120 data points) located on Drexel University campus. The evaluation included a preliminary off-line or batch analysis to gain a first understanding of the suitability of the various models and their particular drawbacks and then to investigate whether the different chiller models exhibit any time variant or seasonal behavior. The subsequent on-line evaluation consisted of assessing the various models in terms of their suitability for model parameter tracking as well as model prediction accuracy (which would provide the necessary thresholds for flagging occurrence of faults). The former assessment suggested that parameter tracking using the GN model parameters could be a viable option for fault detection (FD) implementation, while the black box models were not at all suitable given their high standard errors. The assessment of models in terms of their internal prediction accuracy revealed that the MLP model was best, followed by the MP and GN models. However, the more important test of external predictive accuracy suggests that all models are equally accurate (CV about 2% to 4%) and, hence, comparable within the experimental uncertainty of the data. ORLS with incremental window scheme was found to be the most robust compared to the other computational schemes. The chiller models do not exhibit any time variant behavior since WRLS was found to be poorest. Finally, in terms of the initial length of training data, it was determined—at least with the data sets used that exhibited high autocorrelation—that about 320 and 400 data points would be respectively necessary for the MP and GN model parameter estimates to stabilize at their long-term values. This paper also provides a detailed discussion of the potential advantages that on-line model training can offer and identifies areas of follow-up research.