Monitoring and Diagnosis of Assembly Fixture Faults Using Modified Multivariate Control Charts and Surface Scanning Content

2007 ◽  
Author(s):  
Jeremy Rickli ◽  
Jaime Camelio
Keyword(s):  
Fault Diagnosis ◽  
Control Charts ◽  
Large Data ◽  
Minimal Amount ◽  
Data Sets ◽  
Surface Scanning ◽  
Statistical Process ◽  
Multivariate Control Charts ◽  
Multivariate Control ◽  
Assembly Fixture

Recent advances in process monitoring technology have introduced an influx of exceptionally large data sets containing information on manufacturing process health. Recorded data sets are comprised of numerous parameters for which multivariate statistical process control (MSPC) methodologies are required. Current multivariate control charts are ideal for monitoring data sets with a minimal amount of parameters, however, new monitoring devices such as surface scanning cameras increase the number of parameters by two orders of magnitude in some cases. This paper proposes a modified form of the original multivariate Hotelling T2 chart possessing the capability to monitor manufacturing processes containing a large number of parameters and a fault diagnosis procedure incorporating least squares analysis in conjunction with univariate control charts. A case study considering surface scanning of compliant sheet metal components and comparisons to processes utilizing Optical CMM’s is presented as verification of the proposed assembly fixture fault diagnosis methodology and modified Hotelling T2 multivariate control chart.

scholarly journals An Application of Univariate and Multivariate Control Charts in Monitoring Water Quality

2020 ◽  
pp. 1-7
Author(s):  
Siti Rahayu Mohd Hashim ◽  
Azwaan Andrew ◽  
Wilter Azwal Malandi
Keyword(s):  
Water Quality ◽  
Control Chart ◽  
Control Charts ◽  
Ph Value ◽  
Statistical Process ◽  
Multivariate Control Charts ◽  
Monitoring Process ◽  
Process Dispersion ◽  
Water Treatments ◽  
Multivariate Control

Control chart is a tool for detecting an out-of-control signal in statistical process control (SPC). It is widely used in process monitoring in order to detect changes in process mean or process dispersion. This study aims to illustrate the application of multivariate control charts in monitoring water quality at one of the water treatments plants in Kota Kinabalu, Sabah. The tested water quality variables in this study are turbidity, pH value, dissolved oxygen (DO) and concentration of ferum. Two multivariate control charts, Hotelling’sT2 and MCUSUM control charts are constructed under the violation of the multivariate normality assumption. The purpose is to study the effect of non-normal data upon the monitoring process using the selected multivariate control charts. By comparing the monitoring process between the two types of control charts, the consistency of the results is studied. All the univariate and multivariate control charts produced out-of-control signals from different points, hence inconclusive results obtained. Keywords: Water quality; multivariate control chart; univariate control chart; Hotelling’s T2; MCUSUM

Improving Phase I Monitoring of Dirichlet Regression Profiles

2018 ◽  
Vol 25 (06) ◽  
pp. 1850030 ◽  
Author(s):  
Hourieh Foroutan ◽  
Amirhossein Amiri ◽  
Reza Kamranrad
Keyword(s):  
Phase I ◽  
Control Chart ◽  
Control Charts ◽  
Compositional Data ◽  
Ratio Test ◽  
Statistical Process ◽  
Explanatory Variables ◽  
Monitoring Procedure ◽  
Multivariate Control

In most statistical process control (SPC) applications, quality of a process or product is monitored by univariate or multivariate control charts. However, sometimes a functional relationship between a response variable and one or more explanatory variables is established and monitored over time. This relationship is called “profile” in SPC literature. In this paper, we specifically consider processes with compositional data responses, including multivariate positive observations summing to one. The relationship between compositional data responses and explanatory variables is modeled by a Dirichlet regression profile. We develop a monitoring procedure based on likelihood ratio test (lrt) for Phase I monitoring of Dirichlet regression profiles. Then, we compare the performance of the proposed method with the best method in the literature in terms of probability of signal. The results of simulation studies show that the proposed control chart has better performance in Phase I monitoring than the competing control chart. Moreover, the proposed method is able to estimate the real time of a change as well. The performance of this feature is also investigated through simulation runs which show the satisfactory performance. Finally, the application of the proposed method is illustrated based on a real case in comparison with the existing method.

scholarly journals Systematic literature review of Brazilian research on multivariate control charts

2021 ◽  
Vol 16 (1) ◽  
pp. 122-149
Author(s):  
Renan Mitsuo Ueda ◽  
Leandro Cantorski da Rosa ◽  
Wesley Vieira da Silva ◽  
Ícaro Romolo Sousa Agostino ◽  
Adriano Mendonça Souza
Keyword(s):  
Literature Review ◽  
Bibliometric Analysis ◽  
Systematic Literature Review ◽  
Control Charts ◽  
Design Methodology ◽  
Web Of Science ◽  
Multivariate Control Charts ◽  
Know How ◽  
Multivariate Control ◽  
The Web

Purpose – This paper aims to present a Systematic Literature Review (SLR) of studies in Brazil with applications of multivariate control charts indexed in journals on the Web of Science. Design/methodology/approach – The following steps were carried out: a detailed synthesis was performed on the general characteristics of the corpus, co-citation and collaboration networks analyzed; and a co-occurrence of terms in the text corpus was verified. A Systematic Literature Review was carried out using the protocols set out by Biolchini et al. (2007), Kitchenham (2004) and Tranfield, Denyer and Smart (2003). Papers were selected from the Web of Science database, and after applying filters, results for 29 articles were given to compose the corpus. Findings – A tendency was found for an increase in publications, along with more international research on the issue. The journal most used for publication was the Microchemical Journal. This analysis provided relevant authors for research in this area: Harold Hotelling, Douglas Montgomery, and John Frederick MacGregor. Important Brazilian researchers were highlighted who work mainly in the pharmaceutical and biodiesel industry. Originality/value – No articles were found that had carried out a Systematic Literature Review of Brazilian research on multivariate control charts. The main contributions to this manuscript related to an increase in scientific know-how in the area of multivariate and bibliometric analysis. Keywords - Multivariate Control Charts. Systematic literature review. Bibliometric analysis.

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