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Process monitoring and fault diagnosis are of great importance for operation safety and efficiency of complex industrial plants. The present article proposes a novel methodology to address the sensor location problem for fault detection. Firstly, all the process situations are identified based on a fuzzy learning algorithm using measurements generated from the whole available set of sensors. Then, a fuzzy feature selection approach is used to select the optimal number of sensors that characterize accurately the set of process situations (abnormal and normal). This method optimizes the performance of the learning algorithm within a membership margin framework, and thereby, it is capable to address correlation and redundancy issues. A behavioral pattern of the process is constructed with the selected sensors and is used to associate new online observations to previously characterized process situations. The proposed strategy has been applied for fault diagnosis to a pharmaceutical synthesis carried out in a new intensified heat-exchanger reactor.