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In statistical process monitoring, contribution plots are commonly used by operators and experts to identify the root cause of abnormal events. Because contribution plots suffer from fault smearing - an effect that possibly masks the cause of an upset - this paper investigates whether automated fault identification can be improved by using process data instead of contributions. Hereto, both approaches (i.e., using either the sensor measurements or their contributions as inputs for a classification model) are tested on the benchmark penicillin fermentation process Pensim, implemented in RAYMOND. To optimize the performance of each approach, different manipulations of both the process data and the variable contributions are introduced based on the nature of the occurring faults. It is observed that these manipulations have a large influence on the classification performance. Furthermore, this paper demonstrates that fault smearing negatively affects the classification based on the variable contributions. It is concluded that automated fault identification is improved by using the process data rather than the variable contributions as model inputs for the case study investigated.

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Fault Identification in Batch Processes Using Process Data or Contribution Plots: A Comparative Study∗∗Work supported in part by Project PFV/10/002 (OPTEC Optimization in Engineering Center) of the Research Council of the KU Leuven, Project KP/09/005 (SCORES4CHEM) of the Industrial Research Council of the KU Leuven, and the Belgian Program on Interuniversity Poles of Attraction initiated by the Belgian Federal Science Policy Office. The authors assume scientific responsibility.