New Approaches for Monitoring Image Data

Okhrin, Yarema; Schmid, Wolfgang; Semeniuk, Ivan

doi:10.1109/tip.2020.3039389

Cited by 6 publications

(3 citation statements)

References 40 publications

(61 reference statements)

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“…When a change occurs at time 𝜏, 𝐿 1 deviates more from 𝐿 0 and as a result, the value of ratio 𝐿 1 ∕𝐿 0 increases. Consequently, 𝐿 1 ∕𝐿 0 can be utilized to identify the out-of-control condition in the process as shown in Equation (19).…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…If fault occurs between the regions, the performance of the method will deteriorate in detecting out-of-control signals. Okhrin et al 19 proposed a control chart based on regions of interest for dimension reduction and maintained the multi dimensionality of the data. Bui and Apley 20 proposed an approach based on supervised learning algorithms to detect surface texture related defects.…”

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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Section: The Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Two‐dimensional wavelet based statistical monitoring of image data

Koosha

Noorossana

Ahmadi

2022

Quality & Reliability Eng

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“…Therefore, there is a consequent need for some ML-based approaches for the successful monitoring of image processes. The existing methods, for instance, Okhrin et al 155 and Yuan and Lin 156 , can be improved to developing CNN and Transformers control charts to monitoring images in SM.…”

Section: Monitoring Image Datamentioning

confidence: 99%

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

Tran¹,

Nadi

Nguyễn

et al. 2021

Springer Series in Reliability Engineering

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Over the past decades, control charts, one of the essential tools in Statistical Process Control (SPC), have been widely implemented in manufacturing industries as an effective approach for Anomaly Detection (AD). Thanks to the development of technologies like the Internet of Things and Artificial Intelligence (AI), Smart Manufacturing (SM) has become an important concept for expressing the end goal of digitization in manufacturing. However, SM requires a more automatic procedure with capabilities to deal with huge data from the continuous and simultaneous process. Hence, traditional control charts of SPC now find difficulties in reality activities including designing, pattern recognition, and interpreting stages. Machine Learning (ML) algorithms have emerged as powerful analytic tools and great assistance that can be integrating to control charts of SPC to solve these issues. Therefore, the purpose of this chapter is first to presents a survey on the applications of ML techniques in the stages of designing, pattern recognition, and interpreting of control charts respectively in SPC especially in the context of SM for AD. Second, difficulties and challenges in these areas are discussed. Third, perspectives of ML techniquesbased control charts for AD in SM are proposed. Finally, a case study of an ML-based control chart for bearing failure AD is also provided in this chapter.

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Development of control charts to monitor image data using the contourlet transform method

khodadadi,

Owlia,

Amiri

et al. 2023

Quality & Reliability Eng

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In recent years, researchers and practitioners have been exploring new methods for quality control, including image processing. The effective use of high‐volume image data can significantly improve the monitoring of production and service systems in terms of speed, accuracy, and cost. Adopting an image‐based approach is better than relying on operator‐based solutions, and it offers new perspectives for process monitoring. Image processing can involve extracting features to identify, classify, detect, and cluster. Although there are several transformations to extract features from images, the Fourier method cannot consider the concurrency of frequency and time data, and the wavelet method only considers two specific directions. In multidimensional transforms, the optimal method can provide more information using fewer coefficients. The contourlet transform has advantages such as multiresolution, localization, critical sampling, directionality, and anisotropy. This research investigates the advantages of applying the contourlet transform to images and using data in a generalized likelihood ratio control chart. The results show that this method is more accurate than others because it can examine various directions in images. The proposed methodology algorithm is also presented in this study.

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New Approaches for Monitoring Image Data

Cited by 6 publications

References 40 publications

Two‐dimensional wavelet based statistical monitoring of image data

Two‐dimensional wavelet based statistical monitoring of image data

Application of Machine Learning in Statistical Process Control Charts: A Survey and Perspective

Development of control charts to monitor image data using the contourlet transform method

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