Fault Detection and Diagnosis for Refrigeration Systems

The theme of cooling is recurrent in the world around us. Air conditioning systems in residential environments are the most common examples of refrigeration systems. However, also in the treatment, storage, transport and distribution of food products, as well as in the health and tertiary sectors, refrigeration plays a central role. The main purpose of this research is the analysis of some techniques for the detection and diagnosis of faults in this type of systems, also called chillers. In the work, the chiller is analyzed in all its components, for which the operating principle and the significant variables for the fault detection task are derived. The research proceeds to the analysis of static methodologies based on the data for the detection of anomalies. Each of them builds a model of the system. This model is then used in the monitoring stage. The static nature of the methods proposed in the thesis refers to the use, in the model identification phase, of data relating to steady state of the system instead of the entire time evolution of the signals. In this way, the system is monitored in conditions of thermodynamic stationarity and sudden transients, difficult to characterize mathematically, are eliminated from the final database. The choice of data-driven methods is consistent with the direction of the current literature, mainly focused on those approaches that do not require a detailed physical description of the system. The ability to fine-tune the model from the data makes these techniques easily applicable to different plants. In particular, the thesis considers three techniques for the detection of anomalies. Two of them, the multiple linear regression and the Principal Components Analysis (PCA), identify a model for the data in the form, respectively, of a surface and a regression hyper-plane, while the third, the Mahalanobis's distance, takes into account the probabilistic characteristics of the dataset. These techniques are generally used for the prediction or for the dimensional reduction. In the thesis their effectiveness is tested in the context of the detection of anomalies. The different philosophies from which they take inspiration and the advantages and disadvantages of each approach are considered. The comparison is proposed for some faulty dataset generated with software and on a real case