Evolutionary support vector regression for monitoring Poisson profiles

Ali Yeganeh · Saddam Akber Abbasi · Sandile Charles Shongwe · Jean‐Claude Malela‐Majika · Alireza Shadman

27 citations0/0 files storedSoft Computing

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Advanced Statistical Process Monitoring Fault Detection and Control Systems Advanced Statistical Methods and Models

Abstract

Abstract Many researchers have shown interest in profile monitoring; however, most of the applications in this field of research are developed under the assumption of normal response variable. Little attention has been given to profile monitoring with non-normal response variables, known as general linear models which consists of two main categories (i.e., logistic and Poisson profiles). This paper aims to monitor Poisson profile monitoring problem in Phase II and develops a new robust control chart using support vector regression by incorporating some novel input features and evolutionary training algorithm. The new method is quicker in detecting out-of-control signals as compared to conventional statistical methods. Moreover, the performance of the proposed scheme is further investigated for Poisson profiles with both fixed and random explanatory variables as well as non-parametric profiles. The proposed monitoring scheme is revealed to be superior to its counterparts, including the likelihood ratio test (LRT), multivariate exponentially weighted moving average (MEWMA), LRT-EWMA and other machine learning-based schemes. The simulation results show superiority of the proposed method in profiles with fixed explanatory variables and non-parametric models in nearly all situations while it is not able to be the best in all the simulations when there are with random explanatory variables. A diagnostic method with machine learning approach is also used to identify the parameters of change in the profile. It is shown that the proposed profile diagnosis approach is able to reach acceptable results in comparison with other competitors. A real-life example in monitoring Poisson profiles is also provided to illustrate the implementation of the proposed charting scheme.

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References

1987An Introduction to Statistical Quality Control.2000On-Line Monitoring When the Process Yields a Linear Profile 2007Current research on profile monitoring 2007Monitoring General Linear Profiles Using Multivariate Exponentially Weighted Moving Average Schemes 2007Statistical monitoring of nonlinear product and process quality profiles 2006Performance evaluation of two methods for online monitoring of linear calibration profiles 2008Monitoring Profiles Based on Nonparametric Regression Methods 2009Profile monitoring for a binary response

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