A novel framework for spatiotemporal monitoring and post‐signal diagnosis of processes with image data

Amirkhani, Farzad; Amiri, Amirhossein

doi:10.1002/qre.2600

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…In this sub‐section, the performance of the proposed method is compared against two existing methods proposed by Koosha et al 22 . and Amirkhani and Amiri 18 …”

Section: Simulation Studymentioning

confidence: 99%

“…Codes for simulation and regeneration of the results for the methods proposed by Koosha et al 22 . and Amirkhani and Amiri 18 can be accessed at https://github.com/mehdikoosha/statistical-process-monitoring-via-image-data-using-wavelets and https://github.com/fzdamirkhani/QREI-2019, respectively.…”

Section: Simulation Studymentioning

confidence: 99%

“…Zou et al 17 proposed an EWMA and region growing control charting method for monitoring of processes in which uniformity, or a specific pattern is of interest. Amirkhani and Amiri 18 proposed a p-value based control chart to compare the residual matrix of the intensity values of the nominal and captured images to detect the location of the shift. The weakness of this method is its dependency to shift location.…”

Section: Introductionmentioning

confidence: 99%

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Two‐dimensional wavelet based statistical monitoring of image data

Koosha

Noorossana

Ahmadi

2022

Quality & Reliability Eng

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Statistical process monitoring techniques are widely used to monitor process stability. In high volume and highly automated processes, effective methods are required to monitor several quality characteristics of interest. Advances in imaging technology have paved the grounds to capture and analyze product images from different perspectives in real time. Advantages of considering images have led to their increasing role in process monitoring. When images are used for process monitoring, complicated data structures should be analyzed. Various methods including data transformation and dimension reduction have been proposed in the literature to cope with these complexities. In this paper, a two‐dimensional (2D) wavelet based method is proposed to monitor image data. A likelihood ratio test (LRT) is developed to monitor the expected values of wavelet coefficient, and the derived likelihood ratio based statistic is monitored over time to detect out‐of‐control conditions as well as change point estimation. This information can help practitioners to diagnose the process, find the potential assignable cause(s) and return the process to the in‐control state. One of the advantages of the proposed method compared to other competing methods is that the performance of the proposed method does not depend on the fault location. Simulation studies indicate satisfactory performance for the proposed method in detecting out‐of‐control conditions and estimating change points. Moreover, the performance of the proposed method is also compared to two existing approaches. The results reveal that the proposed approach performs superior in detecting either diagonal or square defects.

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“…In this sub‐section, the performance of the proposed method is compared against two existing methods proposed by Koosha et al 22 . and Amirkhani and Amiri 18 …”

Section: Simulation Studymentioning

confidence: 99%

Section: Simulation Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Two‐dimensional wavelet based statistical monitoring of image data

Koosha

Noorossana

Ahmadi

2022

Quality & Reliability Eng

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“…In this context a data image is taken from a process, and using 2D wavelet transformations features are extracted and a statistic can be derived to monitor and plot on the control chart. Wavelet analysis has been widely used in image processing and we expect more papers on the use of advanced image processing using wavelet to be applied to image statistical process control (Amirkhani and Amiri, 2020;Zuo et al, 2019).…”

Section: Some Research Directionsmentioning

confidence: 99%

On wavelet-based statistical process monitoring

Cohen

Atoui

2020

Transactions of the Institute of Measurement and Control

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This paper presents an overview of wavelet-based techniques for statistical process monitoring. The use of wavelet has already had an effective contribution to many applications. The increase of data availability has led to the use of wavelet analysis as a tool to reduce, denoise, and process the data before using statistical models for monitoring. The most recent review paper on wavelet-based methods for process monitoring had the goal to review the findings up to 2004. In this paper, we provide a recent reference for researchers and engineers with a different focus. We focus on: (i) wavelet statistical properties, (ii) control charts based on wavelet coefficients, and (iii) wavelet-based process monitoring methods within a machine learning framework. It is clear from the literature that wavelets are widely used with multivariate methods compared to univariate methods. We also found some potential research areas regarding the use of wavelet in image process monitoring and designing control charts based on wavelet statistics, and listed them in the paper.

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“…The probability of alarm in a specified period (PASP) and the cumulative PASP are presented to compute the control limits and investigate the monitoring performance. A novel method for spatiotemporal monitoring processes with image data has been presented by Amirkhani and Amiri [10]. In their method, a P-value-based control chart and Dunnett's test are applied to detect the out-of-control condition of process.…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling and monitoring of image data in the presence of temporal and spatial correlations

et al. 2022

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Owing to the increasing sensitivity of the processes and the inadequacy of the methods based on human inspection, use of product images in statistical process control has been considered by some researchers. In this paper, the regression-based approach was developed to monitor image data under two-scale analysis, large-scale and small-scale. Because geometric profiles created from images have complex and non-linear behaviour, wavelet transformation was used to extract the main features (regression coefficients) under large-scale analysis. Observations in mentioned profiles could be temporally or spatially correlated as well. To monitor the small-scale components which could be expressed by correlation in error terms, one parametric and one nonparametric methods were developed. After extracting features for both scales, some appropriate test statistics were computed. Then, monitoring the process was performed by plotting these test statistics on corresponding control charts. Performance of the proposed method was evaluated in terms of run-length and change-point measures. Simulation and industrial case studies were also performed to evaluate the proposed method's performance in detecting different shifts. The results indicated the proper performance of the proposed method in monitoring industrial processes to detect out-of-control conditions and identify the source of variability.

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A novel framework for spatiotemporal monitoring and post‐signal diagnosis of processes with image data

Cited by 10 publications

References 22 publications

Two‐dimensional wavelet based statistical monitoring of image data

Two‐dimensional wavelet based statistical monitoring of image data

On wavelet-based statistical process monitoring

Statistical modeling and monitoring of image data in the presence of temporal and spatial correlations

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