Time Between Events Monitoring with Control Charts

Abstract

Time between events (TBE) control charts play an important role in many applications, particularly in the monitoring of high‐yield processes. In this article, we discuss the most common types of control charts for time between events monitoring, along with some illustrations of their practical implementation. The usual distributional assumption for the time between events is that of the exponential distribution. We also briefly discuss the case where the TBE follows a Weibull distribution. Finally, for both distributions, the cases of known and unknown parameters are discussed.

A Combined Runs Rules Scheme for Monitoring General Inflated Poisson Processes

“…For example, control chart operators are interested in detecting an increase in the expected number of nonconformities of the inspected units or an increase in the expected weekly number of confirmed new infections from a specific disease. In addition, in modern statistical process monitoring (see, for example, [23][24][25][26]), the case of decreases is considered very important because it is related to process improvement. In such cases, the interest is in detecting a decrease in the expected number of non-conformities in the inspected units, which can be considered an indication that the attempts for process improvement have been successful.…”