Authors: Karim Atashgar; Amirhossein Amiri; Mahdi Keramatee Nejad
Addresses: Industrial Engineering Department, Iran University of Science and Technology, Tehran, 16846-13114, Iran ' Industrial Engineering Department, Shahed University, Tehran, 18151-159, Iran ' Industrial Engineering Department, Iran University of Science and Technology, Tehran, 16846-13114, Iran
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.
Keywords: nonlinear profiles; Allan variance; artificial neural network; ANN; statistical process control; SPC; profile monitoring; average run length; ARL.
International Journal of Quality Engineering and Technology, 2015 Vol.5 No.2, pp.162 - 177
Received: 30 Mar 2015
Accepted: 17 Jun 2015
Published online: 09 Sep 2015 *