False alarms rate reduction using filtered monitoring indices

Abstract

False alarms are the major problem in fault detection when using multivariate statistical process monitoring such as principal component analysis (PCA), they affect the detection accuracy and lead to make wrong decisions about the process operation status. In this work, filtering the monitoring indices is proposed to enhance the detection by reducing the number of false alarms. The filters that were used are: Standard Median Filter (SMF), Improved Median Filter (IMF) and fuzzy logic based filter. Signal to Noise Ratio (SNR), False Alarms Rate (FAR) and the detection time of the fault were used as criteria to compare their performance and their filtering action influence on monitoring. The algorithms were applied to cement rotary kiln data; real data, to remove spikes and outliers on the monitoring indices of PCA, and then, the filtered signals were used to supervise the system. The results, in which the fuzzy logic based filter showed a satisfactory performance, are presented and discussed