Adaptive Charts Research Articles

Adaptive EWMA and CUSUM charts with dynamic probability control limits

The utilization of dynamic probability control limits (DPCLs) can prove advantageous in the development of control charts, particularly in scenarios where sample sizes, areas of opportunity, or any other covariate values exhibit temporal fluctuations. In this study, we design the DPCLs-based two adaptive EWMA (AE) and adaptive CUSUM (AC) charts for monitoring the mean of a normally distributed process. We use Monte Carlo simulations to estimate the conditional false alarm rates, zero-state and steady-state (SS) average run-length profiles of the adaptive charts. It is found that the proposed AE and AC charts outperform the existing AE chart in terms of relative mean index. An example is given to demonstrate the implementation of the proposed charts.

Semantic-aware automatic extraction method for bottom sediment annotations in raster nautical charts

ABSTRACT The automatic extraction of bottom sediment annotations in large-scale raster nautical charts has limitations, including an imprecise semantic information description and low efficiency. To overcome them, we propose a convolutional neural network (CNN)-based method for the automatic extraction of bottom sediment annotations in raster nautical charts, using image processing techniques to improve it. First, an adaptive chart partitioning model that considers element completeness is constructed. Second, a principle for the unique identification of elements based on spatial conflicts is designed. Finally, a model for accurately extracting semantic information for bottom sediment annotations is established. To evaluate the effectiveness of the proposed method, we implemented a model based on the PyTorch framework and used the PIL library to analyze the results. We also conducted comparative experiments on multiple CNN models to recommend the selection of such models in the proposed method by comparing their classification and recognition performance. The experimental results indicate that (1) the proposed model can achieve high-precision extraction of bottom sediment annotations in raster nautical charts. (2) Furthermore, the proposed model generally has high recognition accuracy and semantic completeness, with better recognition precision than traditional pattern recognition methods.

Implementation and Adaptability Analysis of Numerical Simulation for Shale Oil CO2 Huff and Puff

Carbon dioxide (CO2) is being considered for use to enhance oil recovery and resource utilization and storage, with wide technical adaptability. In this paper, a numerical simulation method is used to study the adaptability of CO2 huff and puff in shale reservoirs. A fluid model introduces the nanoconfinement effect and reflects the nanoconfinement effect using the fluid p–T phase diagram. This method uses local grid refinement and changes the permeability near the grid to characterize the reservoir reconstruction volume (SRV) fracture network while considering the CO2 diffusion effect. The results indicate that by using the incremental recovery rate and oil change rate as references, adaptive charts can be obtained for different Kf/Km and oil saturation. When Kf/Km is 1000 and the increase in the CO2 recovery rate reaches 1.5%, the lower limit of oil saturation is 0.54. When Kf/Km is 1000 and the increase in the CO2 recovery rate reaches 2%, the lower limit of oil saturation is 0.57. When the oil saturation is 0.5 and the CO2 huff and puff oil change rate reaches 0.3, the lower limit of Kf/Km is 700. Finally, when the oil saturation is 0.548 and the CO2 huff and puff oil change rate reaches 0.6, the lower limit of Kf/Km is 10. The research results are significant and can guide the design and application of on-site CO2 throughput test plans.

Optimal designs of variable sample size control chart for monitoring the multivariate coefficient of variation in short production runs

Short production runs are common in industries that require high flexibility and variety in manufacturing. Only a limited amount of researches on multivariate control charts in short production runs exists in the literature. This article proposes two one-sided adaptive charts for monitoring the multivariate coefficient of variation (MCV) in short production runs by varying the sample size (called the VSS MCV SPR charts). The two one-sided adaptive charts are the upper-sided and lower-sided VSS MCV SPR charts for monitoring increasing and decreasing shifts, respectively, in the process MCV. The optimal parameters of the VSS MCV SPR charts in minimizing the out-of-control truncated average run length (TARL) value, for various shift sizes, are presented. The proposed VSS MCV SPR charts outperform their non-adaptive short production runs Shewhart MCV counterparts (called the SH MCV SPR charts), as the former have smaller out-of-control TARL value than the latter, for the same shift size. The sensitivity of the VSS MCV SPR charts, in terms of the probability of signaling an alarm within I inspections is also studied, where the VSS MCV SPR charts surpass their corresponding SH MCV SPR charts. An example for implementing the VSS MCV SPR charts is provided.

Design of adaptive EWMA control charts using the conditional false alarm rate

AbstractDynamic control limits can be useful in designing control charts, especially when sample sizes, risk scores, or other covariate values change over time. Computer simulation can be used to control the conditional false alarm rate and thus the in‐control run length properties. We show that this approach can be useful in designing adaptive exponentially weighted moving average (AEWMA) control charts for which the control chart smoothing parameter at a given time point depends on the observed value at that time point. We use AEWMA charts as examples, but the approach can be applied to the adaptive cumulative sum (CUSUM) chart and other types of adaptive charts.

An improved variable parameter mean square error control chart

In order to improve the efficiency of the mean square error control chart in detecting small to moderate shifts, an improved variable parameter mean square error (VPt MSE) control chart is proposed, which uses three different sampling intervals, two different sample sizes and two different control limits. Markov chain method is used to calculate the average time to signal and average number of observations to signal. The performance of the adaptive chart was compared with the variable sample size and sampling interval (VSSI), the improved variable sample size and sampling interval (VSSIt), the variable parameter (VP), the exponentially weighted moving average (EWMA), and the variable sampling interval exponentially weighted moving average (VSI EWMA) mean square error control chart. Numerical results show that the VPt MSE chart surpasses the other charts in detecting small to moderate shifts. At the same time, we also improve the VPt MSE chart, which greatly improves the statistical performance. An example is provided to illustrate the implementation of the VPt MSE chart.

Monitoring the process coefficient of variation without subgrouping

The coefficient of variation (CV), a measure of relative variability, is an important quality characteristic for a manufacturing process especially when the variance becomes a function of the mean. In this paper, for the first time, we develop four memory-type control charts for monitoring the CV of a normal process using individual observations, namely Crosier CUSUM (CC), EWMA, adaptive CC and adaptive EWMA charts. In addition, the sensitivities of these CV charts are also enhanced via an auxiliary information based CV estimator. The run length characteristics of these control charts are computed using Monte Carlo simulations. The proposed CV charts are also applied on a real dataset related to dowel pins.

Evaluation of Variable Parameter MCV Control Chart in Downward Process Shifts

Previous studies revealed that the coefficient of variation (CV) is important in ensuring process quality, especially for monitoring a process where its process mean and variance are highly correlated. The fact that almost all industrial process monitoring involves a minimum of two or more related quality characteristics being monitored simultaneously. The existing adaptive charts for monitoring the multivariate CV are focused on detecting upward process shifts. The downward process monitoring is crucial since it shows process improvement. Very little research works are available on the downward adaptive multivariate CV chart. This makes it difficult for the quality engineer who wishes to implement the adaptive multivariate CV chart on the downward process monitoring. Therefore, this paper filled the research gap by proposing a downward variable parameter chart for the multivariate coefficient of variation. The performance measures of the proposed charts are derived based on the Markov-chain approach. Numerical comparisons between the proposed and existing charts have been made, in terms of the average time to signal criterion. The findings reveal that the proposed charts outperform the existing charts for detecting small and moderate downward process shifts

A new time adjusting control limits chart for concept drift detection

Time varying dynamic systems whose underlying changing distribution should be continuously monitored to track abnormal behaviors are one of the most recent challenges in many practical applications. When the data arrive in a continuous way, the target concept to be monitored may change accordingly causing a problem of concept drift. Thus, distinguishing between true alarms and changes due to the nonstationarity of the loading data is required. Traditional control charts cannot handle such processes since they do not use a change dependent procedure and they are not designed for concept drift problems. This article proposes the first two-stage time adjusting control chart for monitoring data stream processes with concept drift. Stage I updates the control limits each time an adjustment condition is satisfied based on an incremental linear combination of the historical and the new data. Stage II validates the shift detected in Stage I by testing whether the two subsamples around the drift belong to the same distribution. Experiments based on several drift situations and using different performance measures show that the proposed adaptive chart is more robust than the most recent competitive time varying charts existing in the literature. Moreover, it is more efficient in terms of shift recognition by reducing the false negative detections in several cases.

One-sided variable sampling interval EWMA control charts for monitoring the multivariate coefficient of variation in the presence of measurement errors

Online monitoring of the multivariate coefficient of variation (MCV) can be of interest in many real situations in which the dispersion of a multivariate process is meant to remain constant with regard to its position. To this aim, several control charts have been recently proposed in the literature. In this paper, the new one-sided adaptive charts to monitor the MCV are proposed. The chart applies a variable sampling interval (VSI) strategy on an exponentially weighted moving average (EWMA) scheme, aiming at benefiting from the known advantages of both approaches. Formulas to optimally determine the parameters of the chart are derived and presented. The proposed chart is shown to outperform competing charts under most circumstances. The presence of the measurement errors is considered to understand their effects on the charts. An illustrative example is also included.

Optimal economic statistical design of combined double sampling and variable sampling interval multivariate T 2 control charts

ABSTRACT Recent studies have shown that an adaptive T 2 chart with double sampling and variable sampling interval chart shows a good performance in detecting small to moderate shifts in the mean vector. This paper develops an economic statistical model for the chart. The statistical performance of the proposed establish is evaluated using the average number of false alarms () and the adjusted average time to signal () criteria. The Wald's identity approach is used to derive an expression for the . The unified Lorenzen and Vance's cost model is generalized to establish the economic model. Using numerical examples, we illustrate the application of the proposed model and compare its performance with the other and schemes. A sensitivity analysis is also carried out to investigate the effects of model parameters on the solution of the economic statistical design. The results indicate that the chart economically outperforms the existing standard and other adaptive charts. The economic performance of the chart is also favourable compared to the chart. Abbreviations: SPC Statistical process control; FRS Fixed ratio sampling; DS Double sampling; VSS Variable sample size; VSI Variable sampling interval; VSSI Variable sample size and sampling interval; VP Variable parameters; FA Full adaptive; DSVSI Double sampling and variable sampling interval; MEWMA Multivariate exponentially weighted moving average; ANF Average number of false alarms; ATS Average time to signal; AATS Adjusted average time to signal; ANS Average number of samples; ARL Average run length; SD Statistical design; ED Economic design; ESD Economic statistical design; GA Genetic algorithm

Adaptive EWMA-S2 control charts with adaptive smoothing parameter

Memory charts like EWMA-S2 or CUSUM-S2 are designed to detect a particular change in the process variance efficiently. However, the charts could be inefficient to detect some other shifts. To overcome this constraint, control charts with adaptive schemes that are efficient for a wide range of shifts can be used. This work proposes new adaptive EWMA charts for the dispersion (AEWMA-S2) based on an adaptive smoothing parameter that relates its value to the potential shift of the process. A Markov chain approach is used to optimize its design. A simulation experiment shows the advantage of the proposed charts.

Maximum weighted adaptive CUSUM charts for simultaneous monitoring of process mean and variance

ABSTRACT In this paper, we propose maximum weighted adaptive Crosier CUSUM (MaxWACCUSUM) charts with three different unbiased estimators of the process mean and variance shifts for joint monitoring of the mean and variance of a normal process. The MaxWACCUSUM charts provide an overall good performance for detecting a range of joint mean and dispersion shift sizes. The run length characteristics of the proposed charts are computed using the Monte Carlo simulation method. A detailed comparative study is conducted to compare the run length performances of the proposed and existing charts. It is found that the MaxWACCUSUM charts are more sensitive than the maximum EWMA, double EWMA and CUSUM charts when detecting a range of joint shift sizes. Real datasets are considered to support the theory.

Enhanced operation of wastewater treatment plant using state estimation-based fault detection strategies

Fault detection is essential for monitoring of various biological processes and becomes even more important in that context. This paper, therefore, presents an enhanced tool that merges state estimation with fault detection (FD) methods to improve monitoring of biological processes. The proposed technique, so-called particle filter (PF)-based maximum double adaptive exponential weighted moving average (EWMA) chart, involves two steps. First, the states of the biological processes are estimated using the PF method. In the second step, the faults are detected using the maximum double adaptive EWMA chart. The proposed method is based on the maximum of the absolute values of the EWMA statistics, one monitoring adaptively the variance and the other controlling the mean. The FD performance is studied utilising a wastewater treatment model. The detection performances are assessed in terms of missed detection rate, false alarm rate, detection speed, sensibility to fault sizes and robustness to noise levels.

Design of an EWMA adaptive control chart using MDS sampling

In this article, a non-traditional control chart has been developed for the monitoring of process mean using multiple dependent states sampling whereas the traditional control charts are developed for the fixed sample size. The developed chart utilizes the current as well as the previous information for the effective monitoring of any unusual change in the mean of the targeted quality characteristic of the manufacturing process. The parameters of the proposed chart have been estimated for different process settings. The proposed control chart scheme has been evaluated using the average run lengths of the in-control as well as the out-of-control process through simulation. The comparison of the proposed control chart with existing chart has been studied for different process parameters settings. A practical example has also been included for the illustration of the proposed scheme. It has been shown that the adaptive chart using EWMA statistic is effective in monitoring the mean of the production process.

Economic statistical design of adaptive $$\bar{X}$$ control charts based on quality loss functions

An economic-statistical model is developed for variable parameters $$(VP){\bar{X}}$$ control chart is developed in which the constraints are imposed on the expected times to signal when the process is out of control and the expected number of false alarms per cycle. To improve chart effectiveness, the cost function is extended based upon the Taguchi philosophy of social loss of quality using the common loss functions including the linear, quadratic, exponential, and Linex. All of the loss functions that have been used up so far are symmetric, but what actually happens is that the imposed costs are not the same for over estimation and under estimation of the target value. For this reason, in this paper for the first time in the literature we use the Linex asymmetric loss functions, that results in the least average cost in all adaptive designs, compared to other symmetric loss functions. Using numerical example, we compare the performances of the $$VP{\bar{X}}$$ control charts with the others adaptive charts based on the loss function, and investigate the sensitivity of the chart parameters to changes in process parameters and loss functions. Results indicate a satisfactory performance for the proposed models.

New adaptive control charts for monitoring the multivariate coefficient of variation

Abstract An adaptive control chart is one of the most effective techniques in Statistical Process Control (SPC). The coefficient of variation (CV) is common in many real life applications, especially in manufacturing and materials engineering, finance, medical and biological sciences. This paper proposes three adaptive charts to monitor the multivariate coefficient of variation (MCV), in order to improve the sensitivity of the standard MCV chart, in detecting small and moderate MCV shifts. The proposed charts are designed using the Markov chain approach and they are compared with the existing standard MCV chart using the average time to signal (ATS), standard deviation of the time to signal (SDTS) and expected average time to signal (EATS) criteria. The performance comparison shows that the proposed adaptive MCV charts, particularly the variable sample size and sampling interval (VSSI) MCV chart, outperform the existing MCV charts, in terms of the ATS and EATS criteria. By allowing the sample size and sampling interval to be varied in the VSSI MCV chart, process engineers will have better control in process monitoring and at the same time are able to detect an out-of-control signal quicker. Illustrative examples are presented by considering the VSSI MCV chart to show the chart’s implementation.

Adaptive control charts for skew‐normal distribution

AbstractThe standard Shewhart‐type chart, named FSS‐ chart, has been widely used to detect the mean shift of process by implementing fixed sample and sampling frequency schemes. The FSS‐ chart could be sensitive to the normality assumption and is inefficient to catch small or moderate shifts in the process mean. To monitor nonnormally distributed variables, Li et al [Commun Stat‐Theory Meth. 2014; 43(23):4908‐4924] extended the study of Tsai [Int J Reliab Qual Saf Eng. 2007; 14(1):49‐63] to provide a new skew‐normal FSS‐ (SN FSS‐ ) chart with exact control limits for the SN distribution. To enhance the sensitivity of the SN FSS‐ chart on detecting small or moderate mean shifts in the process, adaptive charts with variable sampling interval (VSI), variable sample size (VSS), and variable sample size and sampling interval (VSSI) are introduced for the SN distribution in this study. The proposed adaptive control charts include the normality adaptive charts as special cases. Simulation results show that all the proposed SN VSI‐ , SN VSS‐ , and SN VSSI‐ charts outperform the SN FSS‐ chart on detecting small or moderate shifts in the process mean. The impact of model misspecification on using the proposed adaptive charts and the sample size impact for using the FSS‐ chart to monitor the mean of SN data are also discussed. An example about single hue value in polarizer manufacturing process is used to illustrate the applications of the proposed adaptive charts.

Economic design of an integrated adaptive synthetic chart and maintenance management system

ABSTRACTControl chart and maintenance management (MM) are two key tools in production management. Although there is a close connection between these tools, they are usually studied separately in both academia and industry. This article develops an integrated model of MM and quality control policy with an adaptive synthetic chart. The corrective maintenance and preventive maintenance are considered. An economical design model that minimizes the general cost of implementing the integrated model is established and solved by a specific genetic algorithm. Numerical experiments are conducted to analyze the optimal policy. The results show that an economic benefit of 2.64% is obtained by the joint optimization model. Finally, the effects of model parameters on the optimal policies are presented through sensitivity analysis, with practical implications for practitioners.

Monitoring the Coefficient of Variation Using a Variable Sampling Interval EWMA Chart

In recent years, the coefficient of variation (CV) chart is receiving increasing attention in quality control. A number of studies demonstrated that adaptive charts could detect process shifts faster than traditional charts. This paper proposes an EWMA chart with variable sampling interval (VSI) to monitor the CV. Formulas for computing the performance measures of the VSI EWMA-γ2 chart are derived using Markov chain, where γ2 denotes the CV squared. Comparative studies show that the VSI EWMA-γ2 chart significantly outperforms other competing charts. An example using real manufacturing data shows that the VSI EWMA-γ2 chart performs well in applications.