"Data Clustering Approach to Industrial Process Monitoring, Fault Detection and Isolation"

isolation, determination of the location and the type of the fault. Fault Detection and Isolation (FDI) – also known by a common name fault diagnosis – can be carried out in many ways. The three logical parts of any FDI scheme are namely detection, decision and isolation, may be partially integrated. Fault detection takes as input the current values of the process measurements and produces one or more fault indicator signals, which are often called residuals. After the detection phase there is an inference mechanism which takes the fault indicator(s) as input and decides whether a fault has occurred or not. Detection of a fault is followed by an isolation phase which carries out identification of the fault. Fault detection methods are divided into two categories: first principles process models and models of process data. In the former approach, physical structure and a priori known relationships between variables of a process form the basis for the construction of the model and observed data are not required. In the latter case, the structure of the model is generic or depends on the data and the model is based on observed data produced by the sensors of the process. However, in practice the line between these two categories is not sharp: measurement data may be used in construction of a first principles model and, correspondingly, a priori knowledge of a process can be used in the construction of a model using process data. Use of datadriven models instead of the first principles models is justified if construction of an accurate first principles model is