In some applications, a relation between a response variable and one or more explanatory variables (referred as a “profile”) characterizes the quality of a process. Profile monitoring is commonly performed through statistical methods, while machine learning schemes have not received much attention in this regard. In this paper, a control chart based on Artificial Neural Networks (ANN) is proposed to monitor linear profiles in phase II. In the proposed control chart, some novel run rules as the major contribution of this paper are also used to enhance the efficiency of the control chart and for faster detection of shifts. Simulation results revealed a good performance of the proposed control chart based on average run length (ARL) criterion. Further, a systematic ANN-based diagnostic procedure was proposed to identify which parameter has changed in the process. Finally, the implementation of the proposed scheme was illustrated through a real calibration example from the field of chemical engineering.

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