China
Africa
Explore chapters and articles related to this topic
Control chart pattern recognition approach to throughput monitoring in sustainable smart manufacturing
Published in Fernando Moreira da Silva, Helena Bártolo, Paulo Bártolo, Rita Almendra, Filipa Roseta, Henrique Amorim Almeida, Ana Cristina Lemos, Challenges for Technology Innovation: An Agenda for the Future, 2017
Patterns in control charts can be difficult to recognise routinely because of possible distortions that may arise as a result of random noises inherent in real-world processes. Purposeful automation of control chart pattern recognition can help alleviate the difficulties. Several approaches for automated CCP recognition now exists and they include rule-based and expert systems (e.g. Lucy-Bouler 1991, Cheng & Hubele 1992, Pham & Oztemel 1992), Artificial Neural Networks (e.g. Guh & Tannock 1999), multiclass support vector machine based classifier (Khormali & Addeh 2016) and hybrid methods for online detection (e.g. Guh 2005). Feature based approaches have also been developed and has been shown to be more flexible in dealing with complex data in comparison to those the use unstructured data (Gauri 2010).
Data Collection and Analysis
Published in James William Martin, Lean Six Sigma for the Office, 2021
The theory behind control charts is that as subsequent samples are taken from the same reference distribution assuming a normal distribution, then 99.73% of them should vary randomly within the control limits. If there are extraneous sources of variation such as outliers, trends, shifts in the mean, or excessive variation, the control chart will show these patterns. In contrast, if the control chart pattern remains symmetrically distributed around its mean and random, then no process adjustments are needed. Control charts differentiate common cause variation (no pattern) from assignable or special cause variation (outliers or a set of observations forming a non-random pattern).
Advances in statistical quality control chart techniques and their limitations to cement industry
Published in Cogent Engineering, 2022
Daniel Ashagrie Tegegne, Daniel Kitaw, Eshetie Berhan
The heuristic approach to Shewhart control chart design is the simplest control chart design that considers statistical criteria and practical experience that recommends the use of samples of size 5, three sigma control limits, and a sampling frequency of 1 h for the - charts. Such a control chart was an effective tool to monitor variability in the parameters and display patterns of the process. The control chart patterns can be normal, increasing trend, decreasing trend, cyclic, systematic, mixture, upward shift, and downward shift patterns. Only the normal pattern is consistent with the hypothesis that the process continues to operate without assignable causes, and all other patterns are not and should not be considered unnatural.