Author:

Keywords:

Science & Technology, Technology, Computer Science, Information Systems, Computer Science, Theory & Methods, Computer Science, Chemometrics, Statistical Process Monitoring (SPM), Fault Detection & Identification (FDI), Batch processes, STATISTICAL PROCESS-CONTROL, BATCH PROCESSES, SIMULATION PACKAGE, DYNAMIC PROCESSES, DIAGNOSIS, ALGORITHMS, SELECTION, DESIGN

Abstract:

© Springer International Publishing Switzerland 2016. A new framework for extending Statistical Process Monitoring (SPM) to simultaneous False Alarm Rejection and Fault Identification (FARFI) is presented in this paper. This is motivated by the possibly large negative impact on product quality, process safety, and profitability resulting from incorrect control actions induced by false alarms— especially for batch processes. The presented FARFI approach adapts the classification model already used for fault identification to simultaneously perform false alarm rejection by adding normal operation as an extra data class. As no additional models are introduced, the complexity of the overall SPM system is not increased. Two case studies demonstrate the large potential of the FARFI approach. The best models reject more than 94% of the false alarms while their fault identification accuracy (> 95%) is not impacted. However, results also indicate that not all classifier types perform equally well. Care should be taken to employ models that can deal with the added classification challenges originating from the introduction of the false alarm class.