On Monitoring High-Dimensional Processes with Individual Observations

Modern data collecting methods and computation tools have made it possible to monitor high-dimensional processes. In this article, Phase II monitoring of high-dimensional processes is investigated when the available number of samples collected in Phase I is limitted in comparison to the number of variables. A new charting statistic for high-dimensional multivariate processes based on the diagonal elements of the underlying covariance matrix is introduced and a unified procedure for Phase I and II by employing a self-starting control chart is proposed. To remedy the effect of outliers, we adopt a robust procedure for parameter estimation in Phase I and introduce the appropriate consistent estimators. The statistical performance of the proposed method is evaluated in Phase II through average run length (ARL) criterion in the absence and presence of outliers and reveals that the proposed control chart scheme effectively detects various kinds of shifts in the process mean. Finally, we illustrate the applicability of our proposed method via a real-world example

A Frontier Based Eco-Efficiency Assessment of Electric Vehicles: The Case of European Union Countries Using Mixed and Renewable Sources of Energy

Electric vehicles (EVs) are seen as a promising solution for creating more efficient and sustainable transportation systems. European Union (EU) members show a strong interest in implementing EVs, and the governments support the concept by offering facilities to the buyers. Although electric vehicles can be operated with nonpolluting fuels, such as natural gas, fuel cells are more efficient. Creating electricity can affect the environment and the economy. Three environmental features (consumption of water, GHG emissions, and energy consumption, plus GDP's contribution to EU gross domestic product) were analyzed for 28 EU member states to measure electric vehicle efficiency. In one of the DEA models, an input-oriented method was employed to compute the efficiency scores. The k-means clustering algorithm defined the high, medium, and low-efficiency groups. Even more so, the total efficiency scores in this study show that using solar energy outperforms mixed-source energy sources was found to be more efficient

Eco-Efficiency Assessment Of Electric Vehicles In The European Union Countries: The Case Of Mix-Sources Of Energy

European Union (EU) member states have considered the environmental impacts of transportation and have prompted Electric Vehicle (EV) usage as one of the technological advancements that could reduce emissions and energy and water consumption. However, this depends on how EVs react to eco-friendly behaviors during their life cycle. The research utilizes a combined life cycle assessment (LCA) and a principal component analysis (PCA) technique to assess the eco-efficiency performance of EVs in EU member states. Considering the energy mix for electricity generation, three environmental indicators (GHG emission, water consumption, and energy consumption) and one economical (contribution to GDP) indicator were used to compute the eco-efficiency scores for 28 EU member states. First, the values for each environmental and economic indicators were obtained. The eco-efficiency scores for each corresponding EU member states were then calculated and compared. From the results of the eco-efficiency analysis, Belgium was found to have the highest eco-efficiency score, while Estonia was tagged to be the least eco-efficient countr

Revisão das cartas de controle multivariadas paramétricas

The current industrial processes with industrial automation and a high volume of data inherent to the processes have required the control of variables in real time, so you can get quick answers to the detection and correction of failures that have occurred during the process. The multivariate nature of the industrial process requires robust methods to obtain effective statistical control. Parametric Multivariate Control charts (CCMP) are widely used in the industrial sector for the monitoring and process control. Parametric multivariate control charts are traditional control charts that presupposes the knowledge of the distribution of variables, for the application of the methods found in the literature. In this article, we discuss the main procedures of CCMP found in literature: Hotelling T ², MEWMA and MCUSUM. As well as its applications in industrial processes. Considering the publications held between 2006 and 2016, surveyed in the main databases of scientific analysis. This article emphasizes the need for review articles, since they represent compiled expositions, inclined to the triggering of new ideas and fields of research. Thus, it is expected that the presented work can serve as a source of motivation for the elaboration of new studies and adaptations of the implementations discussed here.Os processos industriais atuais, dotados de automação industrial e de um alto volume de dados inerentes aos processos têm exigido o controle das variáveis em tempo real, para que seja possível obter respostas rápidas à detecção e correção de falhas ocorridas durante o processo. A natureza multivariável dos processos industriais exigem métodos mais robustos para se obtenha um controle estatístico efetivo. As Cartas de Controle Multivariadas Paramétricas são amplamente utilizadas no setor industrial para o monitoramento e controle de processos. As Cartas de Controle Multivariadas Paramétricas são cartas de controle tradicionais que pressupõe o conhecimento da distribuição das variáveis, para aplicação dos métodos encontrados na literatura. Neste artigo, discutimos os principais procedimentos de CCMP encontrados na literatura: Hotelling T², MEWMA e MCUSUM. Assim como suas aplicações nos processos industriais. Considerando as publicações realizadas entre 2006 e 2016, pesquisadas nas principais bases de dados de análise científica. Enfatiza-se neste artigo a necessidade de artigos de revisão, pois estes representam estudos expostos de forma compilada inclinando-se ao desencadeamento de novas ideias e campos de pesquisa. Assim, espera-se que o trabalho apresentado possa servir como fonte de motivação para elaboração de novos estudos e adaptações das implementações aqui discutidas

Sustainability Assessment in Aviation Industry: A Mini- Review on the Tools, Models and Methods of Assessment

Sustainable aviation practices have significantly reduced Greenhouse Gas (GHG) emissions over the years. However, these practices have not shaped the aviation industry in achieving the United Nations Sustainable Development Goals (UN SDGs) to its full potential. The increasing volume of air traffic and the benefits reaped in this sector has hindered sustainable airline operations. This paper brings up a small scale-literature review on several tools and methods used for sustainability assessment in the aviation industry to address this concern, covering all the socio-economic and environmental sustainability dimensions. A review of various models and techniques used in the eco-efficiency assessment for sustainable airline operations are also discussed. Decision Support Systems (DSS) using Artificial Intelligence (AI), Deep learning, and Neural Network (NN) models also formed the basis of this study. This paper's tools and models support strategic and tactical decision-making to foster sustainable operations in the aviation industry; thus, helping mitigate the current challenges

Seleção de variáveis aplicada ao controle estatístico multivariado de processos em bateladas

A presente tese apresenta proposições para o uso da seleção de variáveis no aprimoramento do controle estatístico de processos multivariados (MSPC) em bateladas, a fim de contribuir com a melhoria da qualidade de processos industriais. Dessa forma, os objetivos desta tese são: (i) identificar as limitações encontradas pelos métodos MSPC no monitoramento de processos industriais; (ii) entender como métodos de seleção de variáveis são integrados para promover a melhoria do monitoramento de processos de elevada dimensionalidade; (iii) discutir sobre métodos para alinhamento e sincronização de bateladas aplicados a processos com diferentes durações; (iv) definir o método de alinhamento e sincronização mais adequado para o tratamento de dados de bateladas, visando aprimorar a construção do modelo de monitoramento na Fase I do controle estatístico de processo; (v) propor a seleção de variáveis, com propósito de classificação, prévia à construção das cartas de controle multivariadas (CCM) baseadas na análise de componentes principais (PCA) para monitorar um processo em bateladas; e (vi) validar o desempenho de detecção de falhas da carta de controle multivariada proposta em comparação às cartas tradicionais e baseadas em PCA. O desempenho do método proposto foi avaliado mediante aplicação em um estudo de caso com dados reais de um processo industrial alimentício. Os resultados obtidos demonstraram que a realização de uma seleção de variáveis prévia à construção das CCM contribuiu para reduzir eficientemente o número de variáveis a serem analisadas e superar as limitações encontradas na detecção de falhas quando bancos de elevada dimensionalidade são monitorados. Conclui-se que, ao possibilitar que CCM, amplamente utilizadas no meio industrial, sejam adequadas para banco de dados reais de elevada dimensionalidade, o método proposto agrega inovação à área de monitoramento de processos em bateladas e contribui para a geração de produtos de elevado padrão de qualidade.This dissertation presents propositions for the use of variable selection in the improvement of multivariate statistical process control (MSPC) of batch processes, in order to contribute to the enhacement of industrial processes’ quality. There are six objectives: (i) identify MSPC limitations in industrial processes monitoring; (ii) understand how methods of variable selection are used to improve high dimensional processes monitoring; (iii) discuss about methods for alignment and synchronization of batches with different durations; (iv) define the most adequate alignment and synchronization method for batch data treatment, aiming to improve Phase I of process monitoring; (v) propose variable selection for classification prior to establishing multivariate control charts (MCC) based on principal component analysis (PCA) to monitor a batch process; and (vi) validate fault detection performance of the proposed MCC in comparison with traditional PCA-based and charts. The performance of the proposed method was evaluated in a case study using real data from an industrial food process. Results showed that performing variable selection prior to establishing MCC contributed to efficiently reduce the number of variables and overcome limitations found in fault detection when high dimensional datasets are monitored. We conclude that by improving control charts widely used in industry to accomodate high dimensional datasets the proposed method adds innovation to the area of batch process monitoring and contributes to the generation of high quality standard products

Variable Selection-based Multivariate Cumulative Sum Control Chart

High-dimensional applications pose a significant challenge to the capability of conventional statistical process control techniques in detecting abnormal changes in process parameters. These techniques fail to recognize out-of-control signals and locate the root causes of faults especially when small shifts occur in high-dimensional variables under the sparsity assumption of process mean changes. In this paper, we propose a variable selection-based multivariate cumulative sum (VS-MCUSUM) chart for enhancing sensitivity to out-of-control conditions in high-dimensional processes. While other existing charts with variable selection techniques tend to show weak performances in detecting small shifts in process parameters due to the misidentification of the 'faulty' parameters, the proposed chart performs well for small process shifts in identifying the parameters. The performance of the VS-MCUSUM chart under different combinations of design parameters is compared with the conventional MCUSUM and the VS-multivariate exponentially weighted moving average control charts. Finally, a case study is presented as a real-life example to illustrate the operational procedures of the proposed chart. Both the simulation and numerical studies show the superior performance of the proposed chart in detecting mean shift in multivariate processes.Scopu

Multivariate process variability monitoring for high dimensional data

In today’s competitive market, the quality of a product or service is no longer measured by a single variable but by a number of variables that define the quality of the final product or service. It is known that these quality variables of products or services are correlated with each other, and it is therefore important to monitor these correlated quality characteristics simultaneously. Multivariate quality control charts are capable of such monitoring. Multivariate monitoring of industrial or clinical procedures often involves more than three correlated quality characteristics, and the status of the process is judged using a sample of one size. The majority of existing control charts for monitoring multivariate process variability for individual observations are capable of monitoring up to three quality characteristics. One of the hurdles in designing optimal variability control charts for large dimension data is the enormous computing resources and time that is required by the simulation algorithm to estimate the charts parameters. In this research, a novel algorithm based on the parallelised Monte Carlo simulation has been developed to improve the ability of the Multivariate Exponentially Weighted Mean Squared Deviation (MEWMS) and Multivariate Exponentially Weighted Moving Variance (MEWMV) charts to monitor multivariate process variability with a greater number of quality characteristics. Different techniques have been deployed to reduce computing space and the time complexity taken by the algorithm. The novelty of this algorithm is its ability to estimate the optimal control limit L (optimal L) for any given number of correlated quality characteristics, size of the shifts to be detected based on the smoothing constant, and the given in-control average run length in a computationally efficient way. The optimal L for the MEWMS and MEWMV charts to detect small, medium and large shifts in the covariance matrix of up to fifteen correlated quality characteristics has been provided. Furthermore, utilising the large number of optimal L values generated by the algorithm has enabled us to develop two mathematical functions that are capable of predicting L values for MEWMS and MEWMV charts. This would eliminate the need for further execution of the parallelised Monte Carlo simulation for high dimension data. One of the main challenges in deploying multivariate control charts is to identify which characteristics are responsible for the out-of-control signal detected by the charts, and what is the extent of their contribution to the signal. In this research, a smart diagnostic technique has been developed by using a hybrid of the wrapper filter approach to effectively identify the variables that are responsible for the process faults and to classify the percentage of their contribution to the faults. The robustness of the proposed techniques has been demonstrated through their application to a range of clinical and industrial multivariate processes where the percentage of correct classifications is presented for different scenarios. The majority of the existing multivariate control charts have been developed to monitor processes that follow multivariate normal distribution. In this thesis, the author has proposed a control chart for a non-normal high dimensional multivariate process based on the percentile point of Burr XII distribution. Geometric distance variables are fitted to the subset of correlated quality characteristics to reduce the dimension of the data, which is then followed by fitting the Burr XII distribution to each geometric distance variable. Since individual distance variables are independent, each can be monitored by individual control charts based on the percentile points of the fitted Burr XII distributions. A simulated annealing approach is used to estimate parameters of the Burr XII distribution. The proposed hybrid is utilised to identify and rank the variables responsible for the out-of-control signals of geometric distance variables