Estimating the drift time for processes subject to linear trend disturbance using fuzzy statistical clustering

Although control charts can notify the state of out-of-control in a process by generating a signal, the indication is usually followed by a considerable amount of delay. Identifying the real time of the change in a process would provide a starting point for further investigation of an assignable cause. This paper addresses the problem of detecting the change point in different processes when the quality characteristics drift steadily away from an in-control state. For this purpose, a fuzzy statistical clustering (FSC) method is used to estimate the drift time in different processes. Since the application of an FSC method requires both in- and out-of-control values of the process parameter, a linear regression model is utilised to estimate the trend rate and then calculate the out-of-control process parameter. Through extensive simulations, the performance of the proposed change point estimation method is analysed and compared with the most recent estimators for several control charts. The results demonstrate that the proposed method is more effective in detecting the drift time through a wide range of trend rates. Furthermore, it is shown that the proposed method offers a higher estimation precision compared to conventional statistical methods.