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In this paper, we present a data processing architecture based on a set of cooperative classifiers aimed at class recognition with sensor arrays. The main property of the method is the mitigation of the performance degradation due to drift and fault of individual sensors. Moreover the capability to preserve high classification rate even with a faulty sensor allows replacing and calibrating a sensor during the measurement itself. The algorithm has been positively tested with an experimental dataset with an array of commercial metal oxide semiconductor sensors used to identify a number of volatile compounds

Automatic Fault Identification and On-line Unsupervised Calibration of Replaced Sensors by Means of Cooperative Classifiers