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Conventional image-based process control schemes require distinct, detectable features (e.g., edges or textures). In the absence of such features, current methods fail or require additional preprocessing. They are also prone to false detections from random noise, dirt, or scratches on the surface. In addition, they often focus on pixel intensity differences, making them less effective for subtle shifts or uniform surfaces. Analyzing speckles generated by coherent illumination (e.g., lasers) has been proven superior in situations where the surface has subtle or no features. Components with smooth surfaces (e.g., gears) lack distinct image features, making traditional methods ineffective. Laser speckle image provides unique dynamic and surface-sensitive insights that are not attainable with conventional imaging. The robustness of speckles to lighting and contamination makes speckle analysis even better suited for industrial environments. In addition, speckle patterns allow for fine-grained analysis of specific regions. This study introduces speckle pattern analysis for control charting and process control. The developed scheme is also capable of simultaneously detecting shifts that occur in different parts of the speckle pattern. It has two main steps. In the first step, we develop an EMWA control chart based on the maximum absolute Fourier magnitude spectrum differences of the current image (speckle pattern) and the reference speckle pattern. In the second step, which is performed when the chart signals, we use a p value analysis to identify the shifted areas of the image. Our scheme relies on dividing the image into equal-sized grids, analyzing them individually, and then aggregating the results. We use a gear's case for our numerical analyses and conduct simulation runs under different shift sizes and gridding scenarios. Finally, we illustrate how the developed scheme can be used for online process monitoring.

Speckle metrology is a powerful optical sensing tool for non-destructive testing (NDT) and advanced surface characterization, enabling ultra-precise measurements of surface deformations and displacements. These capabilities are critical for material analysis and surface assessment in sensing-driven applications. However, traditional correlation methods often struggle to balance resolution and robustness, particularly when simultaneously measuring both small- and large-scale deformations in noisy, high-frequency data environments. In this paper, we present an adaptive resolution approach for speckle displacement measurement that combines grid-based phase correlation with statistical refinement for enhanced accuracy and resolution.

Unlike traditional phase correlation techniques that rely on global correlation, our method introduces a flexible grid-based framework with localized correlation and dynamic overlap adjustments. To improve measurement performance, we developed an optimization technique that uses the median absolute deviation of residuals between reference and deformed images, enabling the algorithm to automatically adapt grid sizes based on local deformation characteristics. This allows it to handle both small- and large-scale deformations simultaneously and effectively. The approach resulted in a relative error reduction of up to 14 % compared to the best of the results obtained using a manually fixed grid size.

The proposed sensing methodology is validated through a series of numerical simulations and experimental studies, including controlled deformations with a micrometer translation stage and random speckle displacements on water-sprayed surfaces. Results demonstrate that our method can accurately detect both known and unknown deformations with high accuracy and precision, outperforming traditional techniques in terms of adaptability and robustness, particularly for surface deformation analysis.

This Letter introduces a novel, to the best of our knowledge, method for assessing fruit quality using white-light biospeckle displacement analysis. The primary aim is to demonstrate that speckle displacement behavior differs between healthy and decaying regions, offering a unique means of gauging bioactivity levels, a feature that has not been explored in previous biospeckle research. By examining speckle displacement vectors over time, significant differences were observed in the displacement patterns between healthy and decaying regions within the fruit tissue. Clustering techniques based on the size and direction of these vectors enabled sharp discrimination between these tissue states. The results indicate that analyzing biospeckle displacement behavior over the observation period provides valuable insights into dynamic processes within the fruit, facilitating a non-invasive evaluation of its quality.

There have been some advances in multivariate control charts in recent years. This paper presents a new simultaneous scheme for monitoring both the mean and variability of a multivariate normal process in a single chart, which is developed by improving and modifying another recently proposed scheme. We not only propose a new control scheme but also make it adaptive by varying all control chart parameters. Our scheme, for the first time, considers the process variability in two forms: "covariance matrix" and "multivariate coefficient of variation (MCV)". This scheme, again for the first time, considers simultaneous monitoring of the MCV with another process parameter (in our case, the mean vector). In addition, we develop a Markov chain model to compute the average run length and average time to signal values. We conduct extensive numerical analyses to measure the performance of the proposed scheme in two scenarios of process variability. At last, we present a numerical example by using a real dataset from a healthcare process to illustrate how the scheme can be implemented in practice.

In certain situations, the quality of a process is determined by dependent variables in relation to independent variables, often modeled through a regression framework referred to as a profile. The practice of monitoring and preserving this relationship is known as profile monitoring. In this paper, we propose an innovative approach that uses different machine-learning (ML) techniques for constructing control charts and monitoring generalized linear model (GLM) profiles with three different GLM-type response distributions of Binomial, Poisson, and Gamma, and by examining different link functions for each response distribution. Through our simulation study, we undertake a comparative analysis of different training methods. We measure the charts’ performance using the average run length, which signifies the average number of samples taken before observing a data point that exceeds the predefined control limits. The result shows that the selection of ML control charts is contingent on the response distribution and link function, and depends on the shift sizes in the process and the utilized training method. To illustrate the practical application of the proposed ML control charts, we present two real-world cases as examples: a drug–response study and a volcano-eruption study, to demonstrate how each ML chart can be implemented in practice.

Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts’ performance and the result shows significant improvements by using the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice.

The combined effect of two real-world-occurring phenomena: ‘measurement errors’ and ‘autocorrelation between observations’ has rarely been investigated. In this paper, it will be investigated for the first time on ‘adaptive’ and/or ’simultaneous monitoring’ charts and also for the first time by using the multivariate linearly covariate measurement errors and VARMA (vector mixed autoregressive and moving average) autocorrelation models, and Markov chains-based performance measures. In addition, this paper for the first time proposes a skip-sampling strategy in an ARMA/VARMA model for alleviating the autocorrelation effect. To do so, we add the above-mentioned measurement errors and autocorrelation models to a recently developed adaptive max-type chart. Then, we develop a Markov chain model to compute the performance measures. After that, extensive numerical analyses will be performed to investigate their combined effect as well as some methods to alleviate their negative effects. Finally, an illustrative example involving a real industrial case will be presented.

The coefficient of variation is a very important process parameter in many processes. A few control charts have been considered so far for monitoring its multivariate counterpart, i.e., the multivariate coefficient of variation (MCV). In addition, autocorrelation is very likely to occur in processes with high sampling frequency. Hence, designing suitable control charts and investigating the effect of autocorrelation on these charts is necessary. However, no control chart has been developed so far for the coefficient of variation that is capable of accounting for autocorrelation in either univariate or multivariate cases. This paper fills the gap by developing multivariate Shewhart-type control charts to monitor MCV with different autocorrelation structures for the observations: vector autoregressive, vector moving average, and vector mixed autoregressive and moving average. In addition, we add variable parameters adaptive features to the Shewhart-type scheme, in order to improve its performance. We develop a Markov chain model to get the statistical performance measures; then, we perform extensive numerical analyses to evaluate the effect of autocorrelation on adaptive and non-adaptive charts in the presence of downward and upward MCV shifts. Finally, we present an illustrative example from a healthcare process to show the implementation of this scheme in real practice.

Simultaneous monitoring of the process parameters in a multivariate normal process has caught researchers’ attention during the last two decades. However, only statistical control charts have been developed so far for this purpose. On the other hand, machine-learning (ML) techniques have rarely been developed to be used in control charts. In this paper, three ML control charts are proposed using the concepts of artificial neural networks, support vector machines, and random forests techniques. These ML techniques are trained to obtain linear outputs, and then based on the concepts of memory-less control charts, the process is classified into in-control or out-of-control states. Two different input scenarios and two different training methods are used for the proposed ML structures. In addition, two different process control scenarios are utilized. In one, the goal is only the detection of the out-of-control situation. In the other one, the identification of the responsible variable (s)/process parameter (s) for the out-of-control signal is also an aim (detection–identification). After developing the ML control charts for each scenario, we compare them to one another, as well as to the most recently developed statistical control charts. The results show significantly better performance of the proposed ML control charts against the traditional memory-less statistical control charts in most compared cases. Finally, an illustrative example is presented to show how the proposed scheme can be implemented in a healthcare process.

In this research, we develop three statistical based control charts: the Hotelling’s T2, MEWMA (multivariate exponentially weighted moving average), and LRT (likelihood ratio test) as well as three machine learning (ML) based control charts: the ANN (artificial neural network), SVR (support vector regression), and RFR (random forest regression), for monitoring generalized linear model (GLM) profiles. We train these ML models with two different training methods to get a linear (regression) output and then apply our classification technique to see if the process is in-control or out-of-control, at each sampling time. In addition to developing the FP (fixed parameters) schemes, for the first time in GLM profiles, we design an adaptive VP (variable parameters) scheme for each control chart as well to increase the charts’ sensitivity in detecting shifts. We develop some algorithms with which the values of the control chart parameters in both FP and VP schemes can be obtained. Then, we develop two algorithms to measure the charts’ performance in both FP and VP schemes, by using the run-length and time-to-signal based performance measures. This is also the first control chart-related research that develops an algorithm to compute the performance measures that applies to any VP adaptive control scheme. After designing the control charts as well as performance measures, we perform extensive simulation studies and evaluate and compare all our control charts under different shift sizes and scenarios, and in three different simulation environments. Finally, we present a numerical example regarding a drug dose-response study to show how the proposed control charts can be implemented in real practice.