An improved process monitoring by mixed multivariate memory control charts: an application in wind turbine field

Abstract

Memory control chart such as multivariate CUSUM (MCUSUM) and multivariate EWMA (MEWMA) control charts are considered superior for the detection of small-to-moderate variation in the process mean vector. In this article, we have proposed two advanced forms of memory multivariate charts to identify the small amount of shifts in the process mean vector. The proposed control charts methodologies are based on the mixed features of the MCUSUM, MEWMA, classical EWMA chart, and chart based on principal component analysis. Monte Carlo simulation technique is used to simulate numerical results. To evaluate the performance of proposed control charts, we have used average run length for a single shift, extra quadratic loss function, relative average run length, and performance comparison index measures for certain range of shifts for overall performance. Results elaborate that proposed charts have outstanding performance for detection of small shifts in mean vector as compared to the various existing such as MCUSUM, MEWMA, etc. charts. For practical purpose, implementation of the proposed control charts with a real-life data in the field of wind turbine has included to make clear the advantages of proposed control chart(s) over other control charts for early detection of shifts.