Multivariate control chart based on PCA mix for variable and attribute quality characteristics

Ahsan, Muhammad; Mashuri, Muhammad; Kuswanto, Heri; Prastyo, Dedy Dwi; Khusna, Hidayatul

doi:10.1080/21693277.2018.1517055

Cited by 29 publications

(34 citation statements)

References 44 publications

(44 reference statements)

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“…The baseline model stems from the statistical characterization of the Q statistic sample in the undamaged state. For that purpose, we employ Kernel density estimation [73,74]. This technique provides a continuous function that accurately fits the distribution of the reference performance indicator sample and constitutes the baseline pattern for damage detection [57,75].…”

Section: Baseline Model Generationmentioning

confidence: 99%

“…In our case, we select a 5% uncertainty level (95% confidence level) to achieve a strong enough SHM assessment tool [11]. The limit value is directly obtained by calculating the 95 percentile of the corresponding kernel density function [73,77,78].…”

Section: Threshold Value Calculationmentioning

confidence: 99%

“…The limit value is directly obtained by calculating the 95 percentile over the corresponding kernel model [73,77,78], being Q limit = 0.9845. Figure 8 shows graphically the limit between the two possible states.…”

Section: Data Processingmentioning

confidence: 99%

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Bearing assessment tool for longitudinal bridge performance

Fernández-Navamuel

Zamora-Sánchez

Alvear

et al. 2020

J Civil Struct Health Monit

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This work provides an unsupervised learning approach based on a single-valued performance indicator to monitor the global behavior of critical components in a viaduct, such as bearings. We propose an outlier detection method for longitudinal displacements to assess the behavior of a singular asymmetric prestressed concrete structure with a 120 m high central pier acting as a fixed point. We first show that the available long-term horizontal displacement measurements recorded during the undamaged state exhibit strong correlations at the different locations of the bearings. Thus, we combine measurements from four sensors to design a robust performance indicator that is only weakly affected by temperature variations after the application of principal component analysis. We validate the method and show its efficiency against false positives and negatives using several metrics: accuracy, precision, recall, and F1 score. Due to its unsupervised learning scope, the proposed technique is intended to serve as a real-time supervision tool that complements maintenance inspections. It aims to provide support for the prioritization and postponement of maintenance actions in bridge management.

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Section: Baseline Model Generationmentioning

confidence: 99%

Section: Threshold Value Calculationmentioning

confidence: 99%

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Bearing assessment tool for longitudinal bridge performance

Fernández-Navamuel

Zamora-Sánchez

Alvear

et al. 2020

J Civil Struct Health Monit

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“…The mean vector and the covariance matrix of the resulting in-control data (also known as the reference data) are used to calculate the control limit, against which data from Phase II can be monitored in real time. For industrial applications, such as chemical batch processes, the in-control data can usually be obtained by ensuring the processes are operating in "normal" conditions (e.g., Ahsan et al 2018;Kini and Madakyaru 2016;Costa et al 2015;Phaladiganon et al 2013). The vast majority of previous research on process monitoring thus comprises Phase II analyses that involve monitoring new data, assuming that Phase I incontrol data is already available (Woodall and Montgomery 2014;Ferrer 2007).…”

Section: A Lack Of Historical In-control Datamentioning

confidence: 99%

Assessing Response Quality by Using Multivariate Control Charts for Numerical and Categorical Response Quality Indicators

Jin

Loosveldt

2020

Journal of Survey Statistics and Methodology

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When assessing interview response quality to identify potentially low-quality interviews, both numerical and categorical response quality indicators (mixed indicators) are usually available. However, research on how to use them simultaneously is very rare. In the current article, we extend the application of conventional multivariate control charts to include response quality indicators that are of a mixed type. We analyze data from the eighth round of the European Social Survey in Belgium, characterized by six numerical and two categorical response quality indicators. First, we employ a principal component analysis mix procedure (PCA Mix) to transform the mixed quality indicators into principal components. The principal component scores are subsequently used to construct a Hotelling T2 statistic. To deal with the non-multivariate normal nature of the principal component scores obtained from the PCA Mix, a nonparametric bootstrap method is then applied to calculate the control limit for the T2 statistic. Second, we suggest tools to interpret an identified outlier in terms of finding the responsible original indicator(s). Third, we present a cyclic procedure for determining the “in-control” data, by iteratively removing the outliers until the process is considered as being in control. Lastly, we identify the most important indicators that discriminate the outliers from the in-control data. Our results imply that multivariate control charts based on relevant projection tools such as PCA Mix in combination with the bootstrap technique have great potential for use in evaluating interview response quality and identifying outliers.

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“…Furthermore, the multivariate control chart is a control chart used to control production process with more than one correlated or uncorrelated characteristics. The latest investigation of the multivariate control chart includes of [10][11][12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

Ahsan¹,

Mashuri²,

Kuswanto³

et al. 2018

Int. J. Adv. Sci. Eng. Inf. Technol.

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Statistical Process Control (SPC) has been widely used in industry and services. The SPC can be applied not only to monitor manufacture processes but also can be applied to the Intrusion Detection System (IDS). In network monitoring and intrusion detection, SPC can be a powerful tool to ensure system security and stability in a network. Theoretically, Hotelling's T 2 chart can be used in intrusion detection. However, there are two reasons why the chart is not suitable to be used. First, the intrusion detection data involves large volumes of high-dimensional process data. Second, intrusion detection requires a fast computational process so an intrusion can be detected as soon as possible. To overcome the problems caused by large number of quality characteristics, Principal Component Analysis (PCA) can be used. The PCA can reduce not only the dimension leading a faster computational, but also can eliminate the multicollinearity (among characteristic variables) problem. This paper is focused on the usage of multivariate control chart T 2 based on PCA for IDS. KDD99 dataset is used to evaluate the performance of the proposed method. Furthermore, the performance of T 2 based PCA will be compared with conventional T 2 control chart. The empirical results of this research show that the multivariate control chart using Hotelling's T 2 based on PCA has excellent performance to detect anomaly in network. Compared to conventional T 2 control chart, the T 2 based on PCA has similar performance with 97 percent hit rate. It also requires shorter computation time.

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Multivariate control chart based on PCA mix for variable and attribute quality characteristics

Cited by 29 publications

References 44 publications

Bearing assessment tool for longitudinal bridge performance

Bearing assessment tool for longitudinal bridge performance

Assessing Response Quality by Using Multivariate Control Charts for Numerical and Categorical Response Quality Indicators

Intrusion Detection System Using Multivariate Control Chart Hotelling's T2 Based on PCA

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