Monitoring Allan variance nonlinear profile using artificial neural network approach

Abstract

Profile monitoring is effectively used in a case where the response variable is measured along with the corresponding value of an explanatory variable(s). Profile monitoring allows quality engineers to monitor performance of a process statistically considering a functional relationship at a given time. Although several papers can be found in the literature approached nonlinear profile monitoring, to the best of the authors' knowledge, there is not any researches in monitoring Allan variance nonlinear profile approaching artificial neural network (ANN). ANN capabilities help quality engineers to monitor complex nonlinear profiles in real cases effectively. In this paper an ANN model is proposed to monitor the nonlinear profile of Allan variance. Allan variance is a measure of stability of tools such as oscillator and amplifier. The proposed ANN model not only is capable to identify an out-of-control condition, but also the model is capable to diagnose the parameter(s) responsible to the out-of-control condition. A numerical example is considered to evaluate the performance of the proposed ANN when the process experiences different shift sizes. The evaluation of the performance is investigated using average run length (ARL) and correct classification criteria.