Change point estimation of high-yield processes experiencing monotonic disturbances

• A MLE is proposed to estimate monotonic change points of high-yield processes.
• The proposed estimator can be used without prior knowledge of the exact change type.
• The PAV algorithm is used to estimate the out-of-control parameter of the process.
• The proposed estimator works well under different change types.
• The applicability of the proposed method is illustrated using a real data example.

In this paper, we first propose a maximum likelihood estimator (MLE) of a change point in high-yield processes, where the only assumption is that the change belongs to a family of monotonic changes. Following a signal from the cumulative count of conforming (CCC) control chart, the performance of the proposed monotonic change-point estimator is next evaluated by comparing its performances to the ones designed for step-changes and linear-trend disturbances through extensive simulation experiments involving different single step-changes, linear-trend disturbances, and multiple-step changes. The results show that when the type of change is not known a priori, using the proposed change-point estimator is useful, because it provides accurate and precise estimates of the change points for almost all of the shift magnitudes and all of the change types considered in this paper. In addition, the applicability of the proposed method is illustrated using a real case.