controlling a system with minimum information and regardless of dynamic equations which dominate systems is the aim of intelligent control. one of the common approaches for process control is applying shewhart's quality control charts. neuro-fuzzy networks, as one of the branches of artificial intelligence (ai), can play an effective role in the enforcement of process control's common...

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 appro...

Abstract Self-supervised learning has demonstrated state-of-the-art performance on various anomaly detection tasks. Learning effective representations by solving a supervised pretext task with pseudo-labels generated from unlabeled data provides promising concept for industrial downstream tasks such as process monitoring. In this paper, we present SSMSPC novel approach multivariate statistical ...

Woodall and Montgomery [35] in a discussion paper, state that multivariate process control is one of the most rapidly developing sections of statistical process control. Nowadays, in industry, there are many situations in which the simultaneous monitoring or control, of two or more related quality process characteristics is necessary. Process monitoring problems in which several related variabl...

In this paper we discuss the basic procedures for the implementation of multivariate statistical process control via control charting. Furthermore, we review multivariate extensions for all kinds of univariate control charts, such as multivariate Shewhart-type control charts, multivariate CUSUM control charts and multivariate EWMA control charts. In addition, we review unique procedures for the...

Subspace method identification (SMI) and model reduction for Multivariate Statistical Process Control has been proposed as an improvement to dynamic principal component analysis (DPCA). The linear parametric model structure captures both static and dynamic information from the system. In this paper, an analysis of the dimension reduction capabilities of the subspace approach is provided. It is ...