Principal alarms in multivariate statistical process control

This paper describes a methodology for the simulation of multivariate out of control situations using in-control data. The method is based on finding the independent factors of the variability of the process, and shifting these factors one by one. These shifts are then translated in terms of the observed variables. The shifts provoked by the most important factors are called principal alarms. The principal alarms are plotted, visualizing the main deviations of the process. Also, a resampling procedure for ARL estimation using principal alarms is proposed. An application using a real industrial process, illustrates the usefulness of the methodology

Multivariate Statistical Process Control Charts: An Overview

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 construction of multivariate control charts, based on multivariate statistical techniques such as principal components analysis (PCA) and partial lest squares (PLS). Finally, we describe the most significant methods for the interpretation of an out-of-control signal.quality control, process control, multivariate statistical process control, Hotelling's T-square, CUSUM, EWMA, PCA, PLS

Superpixel-based Semantic Segmentation Trained by Statistical Process Control

Semantic segmentation, like other fields of computer vision, has seen a remarkable performance advance by the use of deep convolution neural networks. However, considering that neighboring pixels are heavily dependent on each other, both learning and testing of these methods have a lot of redundant operations. To resolve this problem, the proposed network is trained and tested with only 0.37% of total pixels by superpixel-based sampling and largely reduced the complexity of upsampling calculation. The hypercolumn feature maps are constructed by pyramid module in combination with the convolution layers of the base network. Since the proposed method uses a very small number of sampled pixels, the end-to-end learning of the entire network is difficult with a common learning rate for all the layers. In order to resolve this problem, the learning rate after sampling is controlled by statistical process control (SPC) of gradients in each layer. The proposed method performs better than or equal to the conventional methods that use much more samples on Pascal Context, SUN-RGBD dataset.Comment: Accepted in British Machine Vision Conference (BMVC), 201

APSS - Software support for decision making in statistical process control

DOI nefunkční (7.1.2019)Purpose: SPC can be defined as the problem solving process incorporating many separate decisions including selection of the control chart based on the verification of the data presumptions. There is no professional statistical software which enables to make such decisions in a complex way. Methodology/Approach: There are many excellent professional statistical programs but without complex methodology for selection of the best control chart. Proposed program in Excel APSS (Analysis of the Process Statistical Stability) solves this problem and also offers additional learning functions. Findings: The created SW enables to link altogether separate functions of selected professional statistical programs (data presumption verification, control charts construction and interpretation) and supports active learning in this field. Research Limitation/implication: The proposed SW can be applied to control charts covered by SW Statgraphics Centurion and Minitab. But there is no problem to modify it for other professional statistical SW. Originality/Value of paper: The paper prezents the original SW created in the frame of the research activities at the Department of Quality Management of FMT, VSB-TUO, Czech Republic. SW enables to link altogether separate functions of the professional statistical SW needed for the complex realization of statitical process control and it is very strong tool for the active learning of statistical process control tasks.Web of Science223261

Statistical Process Control for Monitoring a Diffusion Process

[[abstract]]This study presents a new statistical process control (SPC) procedure for a process together with degradation and diffusion effects. One of such examples is the initial cool-down process of high-pressure hose production. The air temperature readings during the initial cool-down process often exhibit a non-increasing trend with a diffusion effect in that profiles generated from cycle to cycle deviates from each other more over time. A new charting procedure using the Wiener diffusion model is developed in this article. A real data set, generated from the cool-down process of high-pressure hose production, is used to demonstrate the application of proposed method.[[journaltype]]國外[[incitationindex]]EI[[ispeerreviewed]]Y[[booktype]]紙本[[countrycodes]]JP

Modeling in statistical process control

Statistical Process Control (SPC), which is based on statistical theory, helps to monitor the performance of a process. SPC techniques were first introduced by Shewhart [1] in the 1930\u27s. They are used to identify, control and eliminate variation in the process. To control and reduce variation, one should understand it\u27s sources

An Excel Add-In for Statistical Process Control Charts

Statistical process control (SPC) descibes a widely-used set of approaches used to detect shifts in processes in, for example, manufacturing. Among these are "control charts". Control charts and other SPC techniques have been in use since at least the'50s, and, because they are comparatively unsophisticated, are often used by management or operations personnel without formal statistical training. These personnel will often have experience with the popular spreadsheet program Excel, but may have less training on a mainstream statistical package. Base Excel does not provide the ability to draw control charts directly, although add-ins for that purpose are available for purchase. We present a free add-in for Excel that draws the most common sorts of control charts. It follows the development of the textbook of Montgomery (2005), so it may be well-suited for instructional purposes.