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Variability in manufacturing processes must be properly monitored and controlled to avoid incurring quality problems; otherwise, the probability of manufacturing defective products increases, and, consequently, production costs rise. This paper presents the development of a methodology to locate the source(s) of variation in the manufacturing process in case of a statistical deviation so that the user can quickly take corrective actions to eliminate the source of variation, thus avoiding the manufacture of out-of-specification products. The methodology integrates the multivariate cumulative sum control chart and the multilayer perceptron artificial neural network for the detection and interpretation of the source(s) of variation generated in the manufacturing processes. A case study was carried out with a printed circuit board manufacturing process, and it was possible to classify the origin of the variation with a sensitivity of 92.41% and specificity of 91.16%. The results demonstrate the viability of the proposed methodology to monitor and interpret the source of statistical variation present in production systems. [ABSTRACT FROM AUTHOR]

We show that an auxiliary information based (AIB) cumulative sum (CUSUM) chart is equivalent to a special case of the multivariate CUSUM control chart proposed by J. D. Healy. Due to this equivalence, the limitations and restrictions of the AIB-CUSUM chart also apply to Healy's multivariate CUSUM chart. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many modern industrial processes involve multiple quality measures, and using individual control charts for each measure can be misleading if these measures are highly related. This paper proposes a new method for statistically controlling electronic products with multiple, interconnected quality characteristics. The method utilizes a combined model: a multivariate autoregressive (MAR) model with neural networks, to handle the presence of both correlation and autocorrelation in the data. The study compares the effectiveness of MCUSUM, MEWMA, and T² Hotelling charts in detecting small shifts in the overall process quality. To pinpoint the specific variables causing out-of-control signals in the T² Hotelling chart, we introduce a novel decomposition technique. This technique allows us to identify which measures are contributing most to these signals. Additionally, the MCUSUM and MEWMA charts demonstrate excellent performance in detecting small quality changes, leading to faster corrective actions. Overall, these findings suggest that our proposed method can significantly improve the reliability and responsiveness of quality control in electronics manufacturing. [ABSTRACT FROM AUTHOR]

Drill core images offer valuable insights into the texture, structure and mineralogy of ores and their host rocks, which can be used to optimise downstream processes in the mining industry. The impact on downstream processes from particles of similar composition and mineralogy but different textures has been examined by several previous researchers through the application of supervised machine-learning techniques. This study proposes a novel approach for detecting changes in drill core textures through the analysis of optical images. This approach compares three widely used image feature extraction techniques (local binary patterns, grey-level co-occurrence matrix and convolutional neural network), followed by calculation of a uniqueness measure, based on the Hotelling statistic, designed to identify anomalous segments of core. The effectiveness of the uniqueness measure is validated on a test core comprising six sections with different textures. Two drill cores, from the Brukunga test site in South Australia, were selected as case studies. Of the three feature extraction methods, local binary patterns were found to give the strongest signals of change. There exist two main regimes that separate halfway along both drill cores, indicating a change in lithology or the presence of mineralisation. [ABSTRACT FROM AUTHOR]

In multivariate statistical process control, it is as important to identify the variable(s) that cause shift as it is to detect the significant shift in the distribution parameter vector. The important problem with most of the model-based methods developed to detect the shift in the mean parameter of the joint distribution function of multivariate processes is that they require assumptions such as the normality of process distribution and the independence of observations, and therefore do not have a flexible use. In addition to detecting the shift in the mean parameter vector, the current methods for determining the set of variable(s) causing this shift also have an important disadvantage in terms of computational burden, as well as limitations from those assumptions. This study proposes a new data-based method that allows to detect both the shift and the source of the shift using support vector machines and does not have assumption limitations. Simulation study for various process conditions has shown that the proposed method has better performance than traditional methods and can be used flexibly in different process structures. [ABSTRACT FROM AUTHOR]

Variability in manufacturing processes must be properly monitored and controlled to avoid incurring quality problems; otherwise, the probability of manufacturing defective products increases, and, consequently, production costs rise. This paper presents the development of a methodology to locate the source(s) of variation in the manufacturing process in case of a statistical deviation so that the user can quickly take corrective actions to eliminate the source of variation, thus avoiding the manufacture of out-of-specification products. The methodology integrates the multivariate cumulative sum control chart and the multilayer perceptron artificial neural network for the detection and interpretation of the source(s) of variation generated in the manufacturing processes. A case study was carried out with a printed circuit board manufacturing process, and it was possible to classify the origin of the variation with a sensitivity of 92.41% and specificity of 91.16%. The results demonstrate the viability of the proposed methodology to monitor and interpret the source of statistical variation present in production systems.

Reliable monitoring of mineral process systems is key to more efficient plant operation. Multivariate statistical process control based on principal component analysis is well-established in industry but may not be effective when dealing with dynamic nonlinear or transient processes, where process behavior may change rapidly from time to time. Although a large variety of nonlinear models have been proposed to address these problems, the monitoring of complex dynamic process systems remains challenging. Isolation forests are unsupervised machine learning models that provide an interesting approach to process monitoring that has not been explored extensively yet. In this investigation, this approach is compared with traditional multivariate statistical process monitoring based on principal component models. Three real-world case studies are considered. The first case study is based on coal flotation, the second is based on features extracted from a platinum group metal flotation froth; and the third is based on data from an industrial semi-autogenous grinding circuit. In each case, the models were trained on data representing normal operating conditions and then tested on new process data that were generally different from the training data to test their ability to identify these data as out-of-control. The isolation forest models performed better than the principal component models when the data were nonlinear, but not when the data associated with normal operation and faulty conditions were linearly separable, as was the case with the flotation data. [ABSTRACT FROM AUTHOR]

Process monitoring of multivariate quality attributes is important in many industrial applications, in which rich historical data are often available thanks to modern sensing technologies. While multivariate statistical process control (SPC) has been receiving increasing attention, existing methods are often inadequate as they are sensitive to the parametric model assumptions of multivariate data. In this paper, we propose a novel, nonparametric k-nearest neighbours empirical cumulative sum (KNN-ECUSUM) control chart that is a machine-learning-based black-box control chart for monitoring multivariate data by utilising extensive historical data under both in-control and out-of-control scenarios. Our proposed method utilises the k-nearest neighbours (KNN) algorithm for dimension reduction to transform multivariate data into univariate data and then applies the CUSUM procedure to monitor the change on the empirical distribution of the transformed univariate data. Extensive simulation studies and a real industrial example based on a disk monitoring system demonstrate the robustness and effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Classical Hotelling's T 2 control chart is frequently used for monitoring a multivariate process. The existence of outlying observations in the Phase I data used to determine the control limit can have a significant impact on the accuracy of such control charts, as is well known. Based on the robust reweighted orthogonalized comedian estimates, a robust multivariate quality control chart for individual observations is proposed in this study. Control limit of the proposed robust control chart is estimated by modelling the simulated quantiles for any sample size. A Simulation study has been conducted to examine the performance of the proposed method and compare it with the performances of the classical Hotelling T 2 control chart, a robust control chart based on shrinkage reweighted estimator and two robust control charts based on the reweighted minimum covariance determinant estimator. The results demonstrate the effectiveness of the proposed method, even under high-dimensional settings or when the contamination in the Phase I data is high, with both independent and correlated variables. Performance of the proposed method is also illustrated by implementing in a real-world example [ABSTRACT FROM AUTHOR]

Parametric multivariate control charts like Hotelling's T 2 , rely on certain distributional assumptions that if these assumptions are not met, it leads us to an excessive number of false alarms and reduces the effectiveness of the monitoring strategy. In contrast, nonparametric multivariate control charts, specifically data-depth-based charts and related inferences, do not require any distributional assumptions and no constraints on data dimensions. In this paper, based on a family of multivariate affine invariant distribution-free data-depth-based tests for the multivariate one-sample location problem, a Shewhart-type nonparametric multivariate phase-II control chart is proposed. The performance of the proposed control chart is evaluated with a Monte Carlo simulation study using the average run length measure and is compared to that of competitors. It is observed that the proposed control chart maintained a desirable performance and leads to quicker detection of shifts. It is also a close competitor to Hotelling's T 2 chart when the underlying distribution of the process is bivariate normal. Moreover, it performs better than its nonparametric competitor under the bivariate non-normal elliptical direction class of distributions, specifically heavy-tailed, light-tailed, and t-distributions, in a variety of settings. The control chart procedure is illustrated as an application to real-life data. [ABSTRACT FROM AUTHOR]

The fraction nonconforming, which follows a binomial distribution, is one of the most critical quality characteristics of attribute processes. In addition, the multivariate binomial process plays an important role in industries due to the enormous diversity of quality characteristics. A multivariate binomial process is deemed out of control when it triggers a signal in a multivariate statistical process control chart. However, it is difficult to determine which quality characteristic triggers the nonconforming shift. In contrast to most current studies that identify the contributors of shifts in multivariate normal processes, this study discusses the contributors of fraction nonconforming shifts in multivariate binomial processes. First, a test that can be applied to detect outliers in a multivariate binomial distribution is proposed. In addition, a stepwise test method that can be used to determine the contributors of fraction nonconforming shifts in a multivariate binomial process is then developed. Numerical results indicate that the method proposed is effective in determining the contributors of fraction nonconforming shifts for a multivariate binomial process. [ABSTRACT FROM AUTHOR]

Although Phase I analysis of multivariate processes has been extensively discussed, the discussion on techniques for Phase I monitoring of high‐dimensional processes is still limited. In high‐dimensional applications, it is common to observe that a large number of components but only a limited number of them change at the same time. The shifted components are often sparse and unknown a priori in practice. Motivated by this, this article studies Phase I monitoring of high‐dimensional process mean vectors under an unknown sparsity level of shifts. The basic idea of the proposed monitoring scheme is to first employ the false discovery rate procedure to estimate the sparsity level of mean shifts, and then to monitor the mean changes based on the maximum of the directional likelihood ratio statistics over all the possible shift directions. The comparison results based on extensive simulations favor the proposed monitoring scheme. A real example is presented to illustrate the implementation of the new monitoring scheme. [ABSTRACT FROM AUTHOR]

Principal components have been used in conjunction with Hotelling's T2 chart to monitor multivariate processes. It is known that prohibitively large sample sizes are needed to estimate the process parameters with enough precision to deploy the chart. We investigate whether principal components can be used to reduce the dimensionality of a process so that multivariate process control can be performed using estimated parameters. The chart based on the first k principal components, which we will refer to as the TPC,k2$T^2_{{\rm PC},k}$ chart, is investigated. Specifically, we explore three research questions in this paper: (1) can the TPC,k2$T^2_{{\rm PC},k}$ chart with estimated parameters be applied with moderate preliminary sample sizes?, (2) are there situations where the TPC,k2$T^2_{{\rm PC},k}$ charts are able to detect a shift in the mean vector quicker than the T2 charts?, and (3) is it possible to exploit assumptions about the covariance matrix, such as equal covariances, to improve the performance of the TPC,k2$T^2_{{\rm PC},k}$ chart. Using simulation, we find that for high dimensions, the TPC,k2$T^2_{{\rm PC},k}$ chart with estimated parameters can be used to detect shifts in the direction of the first principal component. Otherwise, the chart requires very large preliminary sample sizes. When it is reasonable to assume equal covariances, the number of parameters to be estimated is substantially reduced and the performance of the TPC,k2$T^2_{{\rm PC},k}$ chart is improved. [ABSTRACT FROM AUTHOR]

Fault diagnosis is crucial for realizing safe process operation when a fault occurs. Multivariate statistical process control (MSPC) has widely been adopted for fault detection in real processes, and contribution plots based on MSPC are a well-known fault diagnosis method, but it does not always correctly diagnose the causes of faults. This study proposes a new fault diagnosis method based on the causality between process variables and a monitored index for fault detection, which is referred to as a causal plot. The proposed causal plot utilizes a linear non-Gaussian acyclic model (LiNGAM), which is a data-driven causal inference algorithm. LiNGAM estimates a causal structure only from data. In the proposed causal plot, the causality of a monitored index of fault detection methods, in addition to process variables, is estimated with LiNGAM when a fault is detected with the monitored index. The process variables having significant causal relationships with the monitored indexes are identified as causes of faults. In this study, the proposed causal plot was applied to fault diagnosis problems of a vinyl acetate monomer (VAM) manufacturing process. The application results showed that the proposed causal plot diagnosed appropriate causes of faults even when conventional contribution plots could not do the same. In addition, we discuss the effects of the presence of a recycle flow on fault diagnosis results based on the analysis result of the VAM process. The proposed causal plot contributes to realizing safe and efficient process operations. [ABSTRACT FROM AUTHOR]

Advances in image acquisition technology have made it convenient and economic to collect large amounts of image data. In manufacturing and service industries, images are increasingly used for quality control purposes because of their ability to quickly provide information about product geometry, surface defects, and nonconforming patterns. In production line monitoring, image data often take the form of image streams in the sense that images from the process are being collected over time. In such applications, a fundamental task is to properly analyze image data streams. This image monitoring problem is challenging for several reasons. First, images often have complicated structures such as edges and singularities, which render many traditional smoothing methods inapplicable. Second, a typical grayscale image contains tens of thousands of pixels, so the data is high‐dimensional. It has been shown in the statistical process control (SPC) literature that conventional multivariate control charts have limited power of detecting process shifts when the data dimension is high. In this article, we propose to transform images using a two‐dimensional wavelet basis and monitor the wavelet coefficients by sparse learning‐based multivariate control charts. By adapting the sparse learning algorithm to our quality control problem, the proposed method is able to detect shifts in the wavelet coefficients in a timely fashion and simultaneously identify those shifted coefficients. Combining this feature with the localization property of the wavelet basis, our method also enables accurate diagnosis of faulty image regions. In addition, the proposed charting statistics have explicit formulas, so they are easy to compute. Theoretical justifications and numerical comparisons with an existing method show that our method works well in applications. [ABSTRACT FROM AUTHOR]

Sustainability issues are challenging the cement industry due to its high emission of greenhouse gas, intensive energy consumption, and depletion of resources. One of the strategies to mitigate the problem is to improve process control techniques and optimize resources. The objective of this paper is to survey the approach and evolution of statistical process control chart techniques and study their significance and limitations in the case of optimization of cement production. The main research question this study address is “What are the significances and limitations of statistical process control chart methods to the optimization of cement process?” The methodology of the study followed the literature survey with meta-analysis and focused on identifying the statistical process control chart design techniques and their application to cement industries. The result of the survey indicated that statistical and mathematical algorithms are encapsulated by advanced soft computing methods; however, still, it is the foundation for advanced process control methods. Moreover, it is found that statistical process control has a theoretical and technical gap in the application of the cement industry. The theoretical gap identified in the literature is that in the case of a complex production system the techniques recognize the occurrence of the out-of-control case in the production process but are not able to identify the cause of variation. The technical gap in the statistical process control techniques is that there are several important theoretical control chart techniques, but they are not researched well on how to apply to the real world.

Multivariate Statistical Process Control (MSPC) methods are used commonly to detect and identify shifts in multivariate industrial processes. However, these methods are limited by assumptions and complexities. In this study, a new two-stage MSPC approach based on independent component analysis is proposed. The proposed method aims to provide solutions to both the determination and the identification of the shift in the mean vector of a multivariate process. In the first step of this new method, independent components extracted by ICA were used as monitoring statistics in the MSPC chart to detect the shift in the process. The second step of the method started to deal with the problem of identifying the source of this shift by decomposing the monitoring statistics. The simulation results show the superiority of the new method over traditional methods in both determining and identifying the shift in the process mean vector. [ABSTRACT FROM AUTHOR]

The objective of this article is to carry out a systematic review of the literature on multivariate statistical process control (MSPC) charts used in industrial processes. The systematic review was based on articles published via Web of Science and Scopus in the last 10 years, from 2010 to 2020, with 51 articles on the theme identified. This article sought to identify in which industry the MSPC charts are most applied, the types of multivariate control charts used and probability distributions adopted, as well as pointing out the gaps and future directions of research. The most commonly represented industry was electronics, featuring in approximately 25% of the articles. The MSPC chart most frequently applied in the industrial sector was the traditional T2 of Harold Hotelling (Hotelling, 1947), found in 26.56% of the articles. Almost half of the combinations between the probabilistic distribution and the multivariate control graphs, i.e., 49.4%, considered that the data followed a normal distribution. Gaps and future directions for research on the topic are presented at the end. [ABSTRACT FROM AUTHOR]

Detecting abnormal conditions in manufacturing processes is a crucial task to avoid unplanned downtimes and prevent quality issues. The increasing amount of available high-frequency process data combined with advances in the field of deep autoencoder-based monitoring offers huge potential in enhancing the performance of existing Multivariate Statistical Process Control approaches. We investigate the application of deep auto encoder-based monitoring approaches and experiment with the reconstruction error and the latent representation of the input data to compute Hotelling's T2 and Squared Prediction Error monitoring statistics. The investigated approaches are validated using a real-world sheet metal forming process and show promising results. [ABSTRACT FROM AUTHOR]

Sustainability issues are challenging the cement industry due to its high emission of greenhouse gas, intensive energy consumption, and depletion of resources. One of the strategies to mitigate the problem is to improve process control techniques and optimize resources. The objective of this paper is to survey the approach and evolution of statistical process control chart techniques and study their significance and limitations in the case of optimization of cement production. The main research question this study address is “What are the significances and limitations of statistical process control chart methods to the optimization of cement process?” The methodology of the study followed the literature survey with meta-analysis and focused on identifying the statistical process control chart design techniques and their application to cement industries. The result of the survey indicated that statistical and mathematical algorithms are encapsulated by advanced soft computing methods; however, still, it is the foundation for advanced process control methods. Moreover, it is found that statistical process control has a theoretical and technical gap in the application of the cement industry. The theoretical gap identified in the literature is that in the case of a complex produc- tion system the techniques recognize the occurrence of the out-of-control case in the production process but are not able to identify the cause of variation. The technical gap in the statistical process control techniques is that there are several important theoretical control chart techniques, but they are not researched well on how to apply to the real world. [ABSTRACT FROM AUTHOR]

Multivariate statistical process control is used for simultaneously monitoring several process variables. The original artificial contrasts (AC) are very useful for monitoring inhomogeneously distributed data with an indicator variable. The cluster-based AC improve it by considering separated clusters, respectively. Then the artificial data used for the AC overlap each cluster. Numerical experiments show that our method outperforms existing methods in terms of Type-II error rate. [ABSTRACT FROM PUBLISHER]

In a process with a large number of process variables (high-dimensional process), identifying which variables cause an out-of-control signal is a challenging issue for quality engineers. In this paper, we propose an adaptive step-down procedure using conditionalT2statistic for fault variable identification. While existing procedures focus on selecting variables that have strong evidence of a change, the proposed step-down procedure selects a variable having the weakest evidence of a change at each step based on the variables that are selected in previous steps. The information of selected unchanged variables is effectively utilised in obtaining a powerful conditionalT2test statistic for identifying the changed elements of the mean vector. The proposed procedure is designed to utilise the correlation information between fault and non-fault variables for the efficient fault variables identification. Further, the simulation results show that the proposed procedure has the better diagnostic performance compared with existing methods in terms of fault variable identification and computational complexity, especially when the number of the variables is high and the number of fault variables is small. [ABSTRACT FROM AUTHOR]

This study investigates the use of Raman spectroscopy fused with other types of data (e.g., pH, temperature and turbidity) for multivariate statistical process control of two pharmaceutical case studies: one simulated industrial-scale fed-batch process for the production of penicillin and one real lab-scale crystallization process. The monitoring schemes are built on local principal component analysis models and hyper-parameters are tuned with regards to highest accuracy in fault detection. Accuracies above 90% are obtained for all types of data and level of DF. Furthermore, for the first case study the model built solely on spectra achieves higher fault detection rates, when only considering faults that also result in off-specification quality. This is supported by the fact that the fault is not necessarily detected when it occurs, but rather when it starts to affect quality variables as measured by the spectra. • MSPC model based solely on spectra predicts faulty, off-specification batches well • Model built on spectra does not detect faults when they start to affect the quality. • Low-level data fusion and mid-level data fusion achieve similar accuracy • Fault diagnosis easier using contributions to SPE rather than Hotellings T 2 [ABSTRACT FROM AUTHOR]

With the latest advances in computer and networking technologies, the threat of cyber-physical attacks against manufacturing systems is growing. Unlike traditional cyber-attacks, cyber-physical attacks are not limited to intellectual property theft and affect the physical world, which could be devastating to manufacturing, if they are undetected. Relying on traditional quality control to defend against these malicious attacks, manufacturers can choose to either closely monitor a large number of potential quality characteristics or only monitor a specific subset of the characteristics. However, the former choice may be impractical when a large number of potential characteristics exists, whereas the latter might be susceptible to an intelligently designed attack that targets unmonitored characteristics. Therefore, a novel random variable-selection approach that is both resilient to malicious cyber-physical attacks and sensitive to shifts over a small subset of characteristics is proposed in this work. Such an approach is based upon random sampling strategies when using multivariate Hotelling T2 control charts. To assess its usefulness, the proposed approach was compared to an established variable-selection method, using a simplified cost model. The obtained results show that the proposed approach is both cost-effective and well-suited for industrial applications where the number of quality characteristics to monitor is quite significant. [ABSTRACT FROM AUTHOR]

In this work the information provided by a noninvasive imaging sensor was used to develop two algorithms for real time fault detection and product quality monitoring during the Vacuum Freeze-Drying of single dose pharmaceuticals. Two algorithms based on multivariate statistical techniques, namely Principal Component Analysis and Partial Least Square Regression, were developed and compared. Five batches obtained under Normal Operating Conditions were used to train a reference model of the process; the classification abilities of these algorithms were tested on five more batches simulating different kind of faults. Good classification performances have been obtained with both algorithms. Coupling the information obtained from an infrared camera with that of other variables obtained from the PLC of the equipment, and from the textural analysis performed on the RGB images of the product, strongly improves the performances of the algorithms. The proposed algorithms can account for the heterogeneity of the batch and aim to reduce the off-specification products. [ABSTRACT FROM AUTHOR]

Multivariate categorical quality characteristics, whose distribution can be displayed by a contingency table, are routinely encountered in many applications. When most of the cell entries in the contingency table are very small or zeros counts, which is so-called sparse contingency table in the literature, existing methods developed in the literature are often inadequate for use, due to the inaccuracy of the maximum likelihood estimate of its probability distribution, and the inflation of online charting statistics. This paper studies the multivariate statistical process control problem for such sparse contingency table. We integrate the group least absolute shrinkage and selection operator (LASSO) method with the Ridge method to estimate the in-control distribution of a contingency table and propose an efficient EWMA control chart, based on a modified Pearson χ2 statistic, to monitor the changes in it. Numerical results show that our proposed approach has the best overall performance, compared with its competitors. Finally, a real data example is used to demonstrate the effectiveness of the proposed control chart. [ABSTRACT FROM AUTHOR]

Petrochemical companies aim at assessing final product quality in real time, in order to rapidly deal with possible plant faults and to reduce chemical wastes and staff effort resulting from the many laboratory analyses performed every day. In order to answer these needs, the main purpose of the current work is to explore the feasibility of multiblock regression methods to build real-time monitoring models for the prediction of two quality properties of Acrylonitrile-Butadiene-Styrene (ABS) by fusing near-infrared (NIR) and process sensors data. Data come from a production plant, which operates continuously, and where four NIR probes are installed on-line, in addition to standard process sensors. Multiblock-PLS (MB-PLS) and Response-Oriented Sequential Alternation (ROSA) methods were here utilized to assess which of such sensors and plant areas were the most relevant for the quality parameters prediction. Several prediction models were constructed exploiting measurements provided by sensors active at different ABS production process stages. Both methods provided good prediction performances and permitted identification of the most relevant data blocks for the quality parameters’ prediction. Moreover, models built without considering recordings from the final stage of the process yielded prediction errors comparable to those involving all available data blocks. Thus, in principle, allowing final ABS quality to be estimated in real-time before the end of the process itself.

Air pollution poses a major problem in modern cities, as it has a significant effect in poor quality of life of the general population. Many recent studies link excess levels of major air pollutants with health-related incidents, in particular respiratory-related diseases. This introduces the need for city pollution on-line monitoring to enable quick identification of deviations from "normal" pollution levels, and providing useful information to public authorities for public protection. This article considers dynamic monitoring of pollution data (output of multivariate processes) using Kalman filters and multivariate statistical process control techniques. A state space model is used to define the in-control process dynamics, involving trend and seasonality. Distribution-free monitoring of the residuals of that model is proposed, based on binomial-type and generalised binomial-type statistics as well as on rank statistics. We discuss the general problem of detecting a change in pollutant levels that affects either the entire city (globally) or specific sub-areas (locally). The proposed methodology is illustrated using data, consisting of ozone, nitrogen oxides and sulfur dioxide collected over the air-quality monitoring network of Athens. [ABSTRACT FROM AUTHOR]

Copyright of Muthanna Journal of Administrative & Economics Sciences is the property of Muthanna Journal of Administrative & Economics Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Process analytical technologies (PAT) applied to process monitoring and control generally provide multiple outputs that can come from different sensors or from different model outputs generated from a single multivariate sensor. This paper provides a contribution to current data fusion strategies for the combination of sensor and/or model outputs in the development of multivariate statistical process control (MSPC) models. Data fusion is explored through three real process examples combining output from multivariate models coming from the same sensor uniquely (in the near-infrared (NIR)-based end point detection of a two-stage polyester production process) or the combination of these outputs with other process variable sensors (using NIR-based model outputs and temperature values in the end point detection of a fluidized bed drying process and in the on-line control of a distillation process). The three examples studied show clearly the flexibility in the choice of model outputs (e.g. key properties prediction by multivariate calibration, process profiles issued from a multivariate resolution method) and the benefit of using MSPC models based on fused information including model outputs towards those based on raw single sensor outputs for both process control and diagnostic and interpretation of abnormal process situations. The data fusion strategy proposed is of general applicability for any analytical or bioanalytical process that produces several sensor and/or model outputs. [ABSTRACT FROM AUTHOR]

In addition to the quick detection of abnormal changes in a multivariate process, it is also critical to provide an accurate fault identification of responsible components following an out‐of‐control signal. In line with the work of Tan and Shi for diagnosing shifts in the mean vector, this paper develops a Bayesian approach for diagnosing shifts in the covariance matrix. The simulation comparisons favor the proposed approach. A real example is also presented to demonstrate the implementation of the proposed method. [ABSTRACT FROM AUTHOR]

In multivariate statistical process control, it is recommendable to run two individual charts: one for the process mean vector and another one for the covariance matrix. The resulting joint scheme provides a way to satisfy Shewhart's dictum that proper process control implies monitoring both process location and spread. The multivariate quality characteristic is deemed to be out of control whenever a signal is triggered by either individual chart of the joint scheme. Consequently, a shift in the mean vector can be misinterpreted as a shift in the covariance matrix and vice versa. Compelling results are provided to give the quality control practitioner an idea of how joint schemes for the mean vector and covariance matrix are prone to trigger misleading signals that will likely lead to a incorrect diagnostic of which parameter has changed. [ABSTRACT FROM AUTHOR]

In the present article, two semiparametric bivariate control charts are presented, which use order statistics and are effective in jointly monitoring of possible shifts in the process mean and/or variance. To achieve that both the median location (or more generally the location of a specific order statistic) and the number of specific observations of the test sample lying between the control limits are taken into account. The false alarm rate and the in‐control average run length are not affected by the marginal distributions, while the effect of the dependence structure on them is negligible; therefore, they can be used as fully nonparametric charts. A performance‐comparison study is carried out, and an illustrative example is provided using a real‐world data set. [ABSTRACT FROM AUTHOR]

In multivariate statistical process control (MSPC), most multivariate control charts can effectively monitor anomalies based on overall statistic, however, they cannot provide guidelines to classify the source(s) of out-of-control signals. Classifying the source(s) of process mean shifts is critical for quality control in multivariate manufacturing process since the immediate identification of them can greatly help quality engineer to narrow down the set of possible root causes and take corrective actions. This study presents an improved particle swarm optimisation with simulated annealing-based selective multiclass support vector machines ensemble (PS-SVME) approach, in which some selective multiclass SVMs are jointly used for classifying the source(s) of process mean shifts in multivariate control charts. The performance of the proposed PS-SVME approach is evaluated by computing its classification accuracy. Simulation experiments are conducted and a real application is illustrated to validate the effectiveness of the developed approach. The analysis results indicate that the developed PS-SVME approach can perform effectively for classifying the source(s) of process mean shifts. [ABSTRACT FROM PUBLISHER]

A statistical process control framework is proposed to monitor non-linear profiles. The proposed methodology aims at identifying mean shifts or 'shape changes' in a profile. Discrete wavelet transformation (DWT) is applied to separate variation or noise from profile contours. B-splines are adopted to generate critical points to define the shape of a profile. The proposed method is innovative in that users can divide a profile into multiple segments to be monitored simultaneously. The high dimensionality problem that hinders the implementation of multivariate control charts can be solved by this framework. The distance difference statistic for each segment provides diagnostic information when the process of interest is out of control. These proposed statistics form a vector to be fed into any multivariate control chart such as the Hotelling T 2 control chart. A decomposition method can also be applied on the T 2 statistics when an out-of-control profile is detected. A simulation study applied to a forging process is conducted to demonstrate the property of the proposed method. The proposed method is capable of detecting profile shifts and identifying the exact location of problematic segments. [ABSTRACT FROM AUTHOR]

Incidence of heat illness has been increasing dramatically due to the progression of global warming. Preventing severe heat illness, called heatstroke, is crucial because it can lead to long-term multiple organ damage, including the brain, and results in more than 600 deaths per year in the United States. It has been reported that heat stress affects heart rate variability (HRV), which is the fluctuations of the R-R interval (RRI) on an electrocardiogram (ECG). We propose a method for detecting symptoms of heat illness based on HRV analysis in order to prevent exacerbation of heat illness. In the proposed method, monitoring abnormal changes in HRV caused by heat stress is monitored. Multivariate statistical process control (MSPC), a commonly used anomaly detection method in machine learning, is adopted for training the heat illness detection method. To validate the proposed method, we recruited 103 healthy volunteers with risks of heat illness development: employees working in hot environments, athletes, and amateur marathon runners. Data collection was performed using our wearable heart rate sensor and smartphone app. The result of applying the proposed method showed that a sensitivity of 75% (21 out of 28 cases) and a false-positive rate of 1.02 times per hour were achieved. The proposed heat illness detection method will be used in daily life because RRI data can be easily measured by a wearable sensor. The proposed method will contribute to receiving appropriate treatment for heat illness before exacerbation, which contributes to protecting people's health. [Display omitted] • An HRV-based heat illness detection AI algorithm was proposed. • An anomaly detection framework was adopted for constricting the AI. • The proposed AI achieved sensitivity of 75% and FP rate of 1.02 times/h. • We developed a custom wearable heart rate sensor and smartphone app. [ABSTRACT FROM AUTHOR]

An accurate epileptic seizure prediction algorithm can alleviate the problem and reduce risks in the life of a patient suffering from epilepsy. The main motive of this work is to propose a model which can predict seizures well in advance of its occurrence. Multivariate statistical process control (MSPC) has been used for seizure predictions in long-term scalp EEG signal. It has been observed that excessive neuronal activity in the preictal period of seizure changes the electrical characteristic from chaotic to rhythmic behavior. These changes have been utilized for prediction. Eight temporal based features are used for predicting the seizures by using multivariate statistical process control, which is widely known as an anomaly monitoring method. 90 seizures from the CHB-MIT EEG data of ten patients are analyzed. The results of the proposed method demonstrated that 80 seizures out of 90 in preictal period were correctly predicted prior to the seizure onset, thereby giving a sensitivity of 88.89%. The false positive rate is observed to 0.39 per hour. This study proposed a temporal based patient-specific epileptic seizure prediction method using MSPC in long-term scalp EEG signals. It also provides the possibility of realizing an EEG-based epileptic seizure prediction system which requires less computational power. The proposed method does not require preictal data for modeling. The extracted features are computationally easy. The tested result shows good accuracy on the CHB-MIT data base. • Multivariate statistical process control (MSPC) can be used for seizure prediction using EEG signal. • 98% to less than 99% of variation of interictal data should be retained in the PCA model to predict seizures. • Less computational time because of temporal-based feature and MSPC. • Combination of ECG and EEG signal would be future work. [ABSTRACT FROM AUTHOR]

Conventional multivariate control charts usually focus on a specific process shifts range (small or large), and they cannot get the knowledge of manufacturing process through the learning of in-control data and be effective over the whole range of mean shifts, due to the characteristics of their own structures. In this paper, an effective combined multivariate control chart (named CDD chart, i.e., combined D-MCUSUM and D chart) with an adaptive control limit is proposed to improve the overall detection ability of monitoring techniques in multivariate statistical process control. Besides, this paper also provides a basic methodology for designing the adaptive control limit and recommended values of some key parameters (e.g. window size) for a better application. Based on these, a bivariate simulation experiment is conducted to evaluate the performance of the proposed control chart. Simulation results show that the CDD chart offers a better overall performance, compared with regular control charts (e.g. MCUSUM). In addition, a real industrial case also illustrates the effectiveness of the proposed control chart in applications. [ABSTRACT FROM AUTHOR]