This paper introduces a data-based fault diagnosis system that includes an enhanced characterization of faults during transient stages. First, data under abnormal operating conditions (AOC) is projected onto a reference PCA model constructed with data under normal operating conditions (NOC). T2 and Q-statistic measures of this first PCA model are both used to detect the fault and to estimate the duration and delay of its transient evolution. After a dimensionality reduction, a second NOC PCA model is used to process data before diagnosing the faults by standard classification methods such as Artificial Neural Networks (ANN) or Support Vector Machines (SVM). A quantitative validation of the procedure has been carried out using simulated on-line data sets of the Tennessee Eastman Process (TEP). Results indicate that the incorporation of transient data in models improves the overall diagnosis performance, regardless of the particular choice between the statistical methods or the classification methods.

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