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

Multivariate Statistical Process Control Charts and the Problem of Interpretation: A Short Overview and Some Applications in Industry

Woodall and Montgomery 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 variables are of interest are collectively known as Multivariate Statistical Process Control (MSPC). This article has three parts. In the first part, we discuss in brief the basic procedures for the implementation of multivariate statistical process control via control charting. In the second part we present the most useful procedures for interpreting the out-of-control variable when a control charting procedure gives an out-of-control signal in a multivariate process. Finally, in the third, we present applications of multivariate statistical process control in the area of industrial process control, informatics, and businessQuality Control, Process Control, Multivariate Statistical Process Control, Hotelling's T², CUSUM, EWMA, PCA, PLS, Identification, Interpretation

Screwing process analysis using multivariate statistical process control

Screws are widely used for parts joining in industry. The definition of effective monitoring strategies for screwing processes can help to prevent or significantly reduce ineffective procedures, defective screwing and downtime. Monitoring several correlated variables simultaneously in order to detect relevant changes in manufacturing processes is an increasingly frequent practice furthered by advanced data acquisition systems. However, the monitoring approaches currently used do not consider the multivariate nature of the screwing processes. This paper presents the results of a study performed in an automotive electronics assembly line. Screwing process data concerning torque and rotation angle were analyzed using multivariate statistical process control based on principal component analysis (MSPC-PCA). The main purpose was to extract relevant information from a high number of correlated variables in order to early detect undesirable changes in the process performance. A PCA model was defined based on three principal components. The physical meaning of each component was identified, and underlying causes were inferred based on technical knowledge about the process. Monitoring tools, such as score plots and multivariate control charts allowed to detect the defective screwing cases included in the analyzed data set. Furthermore, eight periods of instability were identified. Considering that the out-of-control signals detected in these periods mainly correspond to delays at the beginning of the tightening operation, four potential causes to explain this behavior were ascertained and analyzed. This research allowed to acquire a deeper understanding on the screwing process behavior and about the causes with higher impact on its stability. Due to its flexibility and versatility, it is considered that this approach can be applied to effectively monitor screwing pCEC - Clinical Excellence Commission(undefined

MULTIVARIATE STATISTICAL PROCESS CONTROL FOR CORRELATION MATRICES

Measures of dispersion in the form of covariance control charts are the multivariate analog to the univariate R-chart, and are used in conjunction with multivariate location charts such as the Hotelling T2 chart, much as the R-chart is the companion to the univariate X-bar chart. Significantly more research has been directed towards location measures, but three multivariate statistics (|S|, Wi, and G) have been developed to measure dispersion. This research explores the correlation component of the covariance statistics and demonstrates that, in many cases, the contribution of correlation is less significant than originally believed, but also offers suggestions for how to implement a correlation control chart when this is the variable of primary interest.This research mathematically analyzes the potential use of the three covariance statistics (|S|, Wi, and G), modified for the special case of correlation. A simulation study is then performed to characterize the behavior of the two modified statistics that are found to be feasible. Parameters varied include the sample size (n), number of quality characteristics (p), the variance, and the number of correlation matrix entries that are perturbed. The performance and utility of the front-running correlation (modified Wi) statistic is then examined by comparison to similarly classed statistics and by trials with real and simulated data sets, respectively. Recommendations for the development of correlation control charts are presented, an outgrowth of which is the understanding that correlation often does not have a large effect on the dispersion measure in most cases

Towards federated multivariate statistical process control (FedMSPC)

The ongoing transition from a linear (produce-use-dispose) to a circular economy poses significant challenges to current state-of-the-art information and communication technologies. In particular, the derivation of integrated, high-level views on material, process, and product streams from (real-time) data produced along value chains is challenging for several reasons. Most importantly, sufficiently rich data is often available yet not shared across company borders because of privacy concerns which make it impossible to build integrated process models that capture the interrelations between input materials, process parameters, and key performance indicators along value chains. In the current contribution, we propose a privacy-preserving, federated multivariate statistical process control (FedMSPC) framework based on Federated Principal Component Analysis (PCA) and Secure Multiparty Computation to foster the incentive for closer collaboration of stakeholders along value chains. We tested our approach on two industrial benchmark data sets - SECOM and ST-AWFD. Our empirical results demonstrate the superior fault detection capability of the proposed approach compared to standard, single-party (multiway) PCA. Furthermore, we showcase the possibility of our framework to provide privacy-preserving fault diagnosis to each data holder in the value chain to underpin the benefits of secure data sharing and federated process modeling

Univariate and multivariate control charts for monitoring sugar production process

Quality control is a system to maintain quality of product or service to achieve specification standard of product. One of the most powerful tools is through graphical method which is control chart. This is because control chart easy to analyse the data and able to provide comprehensive information on existing product or process characteristics. There are two types of control chart. First, statistical process control (SPC) and second is multivariate statistical process control (MSPC). Statistical process control commonly referred as SPC, was developed by Dr. Walter A. Shewhart in the mid-1920s. In general, statistical process control is to control and monitor the process of production line and detect abnormal process. However , the Shewhart control chart can only monitor single process variable at a time. Multivariate statistical process control , MSPC was established by Hotelling in his 1947 pioneering paper. MSPC can simultaneously control and monitor more than one process variables at a time. The three most popular multivariate control statistics of multivariate control charts, such as Shewhart charts (x and Range charts) ,cumulative sum plots (CUSUM), and exponentially weighted moving average charts (EMWA)

An integrated MEWMA-ANN scheme towards balanced monitoring and accurate diagnosis of bivariate process mean shifts

Various artificial neural networks (ANN)-based pattern recognition schemes have been developed for monitoring and diagnosis of bivariate process variation in mean shifts. In comparison with the traditional multivariate statistical process control (MSPC) charts, these advanced schemes generally perform better in identifying process mean shifts and provide more effective information towards diagnosing the root causes. However, it seemly less effective for multivariate quality control (MQC) application due to disadvantages in reference bivariate patterns and imbalanced monitoring performance. To achieve ‘balanced monitoring and accurate diagnosis’, this study proposes an integrated multivariate exponentially weighted moving average (MEWMA)-ANN scheme for two-stages monitoring and diagnosis of some reference bivariate patterns. Raw data and statistical features input representations were applied into training of the Synergistic-ANN recognizer for improving patterns discrimination capability. The proposed scheme has resulted in better monitoring – diagnosis performances with smaller false alarm, quick mean shift detection and higher diagnosis accuracy compared to the basic scheme

Investigation of Multivariate Statistical Process Control in R Enviroment

At the first stage of our work, the theoretical knowledge needed to use the multivariate statistical process control (MSPC) was explored. Last year, we clarified the sometimes confused concepts, equations, and formulas [1]. At the se­cond stage, R project simulation studies and some food industrial practical model investigations are carried out for con­firming the MSPC advantages compared with the univariate ones. Furthermore, we analyse, using principal component analysis (PCA), what could cause the outlying values. Moreover, we will demonstrate how to use the MYT-decomposition