Root causality analysis at early abnormal stage using principal component analysis and multivariate Granger causality

Pyun, Hahyung; Kim, Kyeong-Su; Ha, Daegeun; Lee, Chul‐Jin; Lee, Won Bo

doi:10.1016/j.psep.2019.12.010

Cited by 31 publications

(3 citation statements)

References 27 publications

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“…PCA is a useful statistical tool for the source apportionment of trace elements in PM10 of environment protections [8]. It is also widely used in the spatial assessment of water quality parameters [9] and the early detection of process faults in fault detection technologies [10]. PCA reveals that three PCs (e.g., drugs and substance abuse, unemployment, and neglect from parents) explain approximately 52.6% of the total variability of the causes of crimes against the person and are suggested to be retained [11].…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Customs Supervision Competitiveness Using Principal Component Analysis

Lau

Wang

2023

Sustainability

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In order to improve the degree of security and facilitation of the business environment; customs administrations are constantly working to strengthen their own institutional innovation and governance in customs control. As such, this paper establishes an evaluation index of international customs supervision competitiveness based on the eight indexes extracted from the World Customs Organisation (WCO) Revised Kyoto Convention and selects 21 representative national customs using the principal component analysis (PCA) method to assess their competitiveness against SPSSAU quantitatively. Based on the data from the World Economic Forum, World Bank, OECD, WCO Annual Report, and Transparency International, the Dutch customs have relatively the best performance in the range of comprehensive competitiveness, and customs authorities in Germany, New Zealand, the United Kingdom, the United States, Mexico, Australia, the Netherlands, and Singapore also have relatively-best performance under different indexes. Taking China Customs as an example, the gaps between China Customs and the ones with the best performance are also analyzed. In response to the problems identified by the analysis, recommendations are made in the areas of process facilitation, technology application, international cooperation, economic development, taxation management, and capacity building to improve the competitiveness of customs control.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluation of Customs Supervision Competitiveness Using Principal Component Analysis

Lau

Wang

2023

Sustainability

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“…The data preprocessing stage was carried out using principal component analysis (PCA). PCA has been used for the early detection of faults or anomalies in the process industry [35]. The data preprocessing consists of a data scaling process, selecting features using the PCA method, and data division for training and testing the system.…”

Section: Data Preprocessingmentioning

confidence: 99%

Radial basis network estimator of oxygen content in the flue gas of debutanizer reboiler

Sembodo

Effendy

Dwiantoro

et al. 2022

IJECE

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<span>The energy efficiency in the debutanizer reboiler combustion can be monitored from the oxygen content of the flue gas of the reboiler. The measurement of the oxygen content can be conducted in situ using an oxygen sensor. However, soot that may appear around the sensor due to the combustion process in the debutanizer reboiler can obstruct the sensor’s function. In-situ redundancy sensors’ unavailability is a significant problem when the sensor is damaged, so measures must be made directly by workers using portable devices. On the other hand, worker safety is a primary concern when working in high-risk work areas. In this paper, we propose a software-based measurement or soft sensor to overcome the problems. The radial basis function network model makes soft sensors adapt to data updates because of their advantage as a universal approximator. The estimation of oxygen content with a soft sensor has been successfully carried out. The soft sensor generates an estimated mean square error of 0.216% with a standard deviation of 0.0242%. Stochastics gradient descent algorithm with momentum acceleration and dimension reduction using principal component analysis successfully improves the soft sensors’ performance.</span>

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“…This extension allows for the analysis of causal relationships among multiple variables. Subsequently, PCA was combined with MVGC to achieve causal analysis and demonstrate the superiority of the method [16], [17]. Moreover, in older to improve computational efficiency and preserve correct causal relationships, a grouping MVGC algorithm was developed [18].…”

Section: Introductionmentioning

confidence: 99%

Variable Time Delay-Based Granger Causality Approach Integrated with Dynamic Coupling Analysis for Root Cause Diagnosis in Chemical Processes

Li,

Yang,

Huang

et al. 2024

Korean J. Chem. Eng.

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Due to the dynamic characteristics of chemical industry systems, the time delay tends to be variable, which leads to changes in coupling intensity. This is contrary to the assumptions in causal analysis, where the time delay and coupling are typically assumed to be fixed. In this article, a new causal analysis framework that integrates Granger causality with dynamic coupling analysis based on variable time delay is proposed, which not only fully considers the variable time delay in dynamic processes, but also studies the dynamic change of coupling intensity. Firstly, the moving window is used to explore real-time variations in average mutual information to obtain the variable time delay. Then by further analyzing the normal data, the dynamic coupling relationship caused by continuous changes in time delay is distinguished. On this basis, the extended Granger causality and convergent cross mapping are integrated to relax their assumptions of fixed time delay and coupling. Finally, the direction of fault propagation is guided by the results of causal analysis. The effectiveness of proposed mothed is demonstrated by chemical industry case studies.

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Root causality analysis at early abnormal stage using principal component analysis and multivariate Granger causality

Cited by 31 publications

References 27 publications

Evaluation of Customs Supervision Competitiveness Using Principal Component Analysis

Evaluation of Customs Supervision Competitiveness Using Principal Component Analysis

Radial basis network estimator of oxygen content in the flue gas of debutanizer reboiler

Variable Time Delay-Based Granger Causality Approach Integrated with Dynamic Coupling Analysis for Root Cause Diagnosis in Chemical Processes

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