Identification of a real time of a change in a process, when an out-of-control signal is present is significant. This may reduce costs of defective products as well as the time of exploring and fixing the cause of defects. Another popular topic in the Statistical Process Control (SPC) is profile monitoring, where knowing the distribution of one or more quality characteristics may not be appropr...

Quality plays an important role, problems in the number of defects production cause a decrease quality. To prevent this increase defective products, it is necessary to evaluate most types determine causes so that corrective actions are obtained using statistical process control (SPC) methods and with 3 Pareto diagram controllers, charts fishbones. This research intended explore facts about Defe...

The present paper reports a comparative evaluation of four multivariate statistical process control (SPC) techniques for the on-line monitoring of an industrial sugar crystallization process. The process itself is challenging since it is carried out in multiple phases and there exists strong non-linear and dynamic effects between the variables. The methods investigated include classical on-line...

Statistical process control is an excellent quality assurance tool to improve the quality of manufacture and ultimately scores on end-customer satisfaction. SPC uses process monitoring charts to record the key quality characteristics (KQCs) of the component in manufacture. This paper elaborates on one such KQC of the manufacturing of a connecting rod of an internal combustion engine. Here th...

The focus of this work is on Statistical Process Control (SPC) a manufacturing process based available measurements. Two important applications SPC in industrial settings are fault detection and diagnosis (FDD). In work, deep learning (DL) methodology proposed for FDD. We investigate the application an explainability concept (explainable artificial intelligence (XAI)) to enhance FDD accuracy ne...

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...

Process adjustment strategy is an important part of the process improvement methods, which is also called engineering process control (EPC), and it is often integrated with statistical process control (SPC) to improve the process control performance. While feedback control is used to compensate for the output deviation, feedforward control is a proactive control strategy based on a direct measu...

A control chart is one of the tools Statistical Process Control (SPC) used to monitor a production process. The charts ability detect data that crosses border often done using variable transformation. In this paper, we will introduce cumulative sum (CUSUM) based on Link Relative transformation technique regression approach. results showed with was more sensitive compared previous CUSUM and char...

Process monitoring and fault diagnosis using profile data remains an important and challenging problem in statistical process control (SPC). Although the analysis of profile data has been extensively studied in the SPC literature, the challenges associated with monitoring and diagnosis of multichannel (multiple) nonlinear profiles are yet to be addressed. Motivated by an application in multi-op...

When a process demonstrates complex cause-and-effect relationships, adjustments are often used, such as automatic control (APC). On the other hand, statistical (SPC) is used to identify causes of abnormality. For example, we use charts for monitoring. However, in with adjustments, careful consideration should be taken toward choosing characteristics abnormalities may go undetected through sole ...