Statistical process control (SPC) is an approach to evaluate processes whether they are in statistical control or not. For this aim, control charts are generally used. Since sample data may include uncertainties coming from measurement systems and environmental conditions, fuzzy numbers and/or linguistic variables can be used to capture these uncertainties. In this paper, one of the most popula...

It has recently been shown that the performance of multivariate exponentially weighted mean square (MEWMS) and multivariate exponentially weighted moving variance (MEWMV) charts of Huwang et al. (2007) in monitoring the variability of a multivariate process for individual observations is better than existing schemes. Both of these control charts monitor a distinct matrix which is an estimator o...

Condition monitoring is the foundation of a condition based maintenance (CBM). To relate the information obtained from the condition monitoring to the actual state of the system, it is usually required a stochastic model. On the other hand, considering the interactions and similarities that exist between CBM and statistical process control (SPC), the integrated models for CBM and SPC have been ...

Vibration condition monitoring is a non-devastating technique that can be performed to detect tooth cracks propagating in gear systems. This paper proposes apply new methodology using time-domain analysis, frequency-domain and statistical process control charts (SPCC) for crack detection of 10 DOF dynamic model spiral bevel system (SBGS). The mesh stiffness effect used the has been studied anal...

One of the objectives of the research done in statistical process control is to obtain control charts that show few false alarms but, at the same time, are able to detect quickly the shifts in the distribution of the quality variables employed to monitor a productive process. In this paper the synthetic-T control chart is developed, which consists of the simultaneous use of a CRL chart and a Ho...

  Although control charts are very common to monitoring process changes, they usually do not indicate the real time of the changes. Identifying the real time of the process changes is known as change-point estimation problem. There are a number of change point models in the literature however most of the existing approaches are dedicated to normal processes. In this paper we propose a novel app...