Performance of Statistical Control Charts with Bilateral Limits of Probability to Monitor Processes Weibull in Maintenance

Manufacturing with optimal quality standards is underpinned to the high reliability of its equipment and systems, among other essential pillars. Maintenance Engineering is responsible for planning control and continuous improvement of its critical equipment by any approach, such as Six Sigma. This is nourished by numerous statistical tools highlighting, among them, statistical process control charts. While their first applications were in production, other designs have emerged to adapt to new needs as monitoring equipment and systems in the manufacturing environment. The time between failures usually fits an exponential or Weibull model. The t chart and adjusted t chart, with probabilistic control limits, are suitable alternatives to monitor the mean time between failures. Unfortunately, it is difficult to find publications of them applied to the models Weibull, very useful in contexts such as maintenance. In addition, literature limits the study of their performance to the analysis of the standard metric average run length, thus giving a partial view. The aim of this paper is to explore the performance of the t chart and adjusted t chart using three metrics, two unconventional. To do this, it incorporates the concept of lateral variability, in their forms left and right variability. Major precisions of the behavior of these charts allow to understand the conditions under which are suitable: if the main objective of monitoring lies in detecting deterioration, the t chart with adjustment is recommended. On the other hand, when the priority is to detect improvements, the t chart without adjustment is the best choice. However, the response speed of both charts is very variable from run to run