A CNN Approach for Simultaneous Spatiotemporal Fault Interpretation
Sabahno, Hamed LU
(2026)
In Quality and Reliability Engineering International
- Abstract
- Convolutional Neural Networks (CNNs) have emerged as one of the most effective tools for image analysis. In this study, we propose a custom-designed CNN architecture to construct a process control scheme based on image data. The product image is partitioned into equal-sized grids, each comprising three channels: i: reference image, ii: shifted image, and iii: their difference, which are individually input into the CNN. To train the model, we generate synthetic datasets representing both in-control and out-of-control conditions, tailored to reflect the specific nature of the monitoring task. The proposed method offers dual capabilities: it not only detects multiple simultaneous faults in different regions of the image but also localizes the... (More)
- Convolutional Neural Networks (CNNs) have emerged as one of the most effective tools for image analysis. In this study, we propose a custom-designed CNN architecture to construct a process control scheme based on image data. The product image is partitioned into equal-sized grids, each comprising three channels: i: reference image, ii: shifted image, and iii: their difference, which are individually input into the CNN. To train the model, we generate synthetic datasets representing both in-control and out-of-control conditions, tailored to reflect the specific nature of the monitoring task. The proposed method offers dual capabilities: it not only detects multiple simultaneous faults in different regions of the image but also localizes the positions of these faults; all in a single step. Performance evaluation is conducted using run length metrics for detection effectiveness and the Dice score for fault localization accuracy. Extensive simulation studies are carried out to assess the scheme’s performance under various shift magnitudes and spatial configurations. Comparative analysis with recently developed control schemes demonstrates the superior performance of our approach in detection, localization, or both across numerous scenarios. Finally, we provide a practical case study to illustrate the implementation of the proposed method in a real practice. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/81ad7b4b-dd59-4ecb-b371-1e53d7d9262e
- author
- Sabahno, Hamed
LU
- organization
- publishing date
- 2026
- type
- Contribution to journal
- publication status
- epub
- subject
- keywords
- Convolutional neural networks (CNN), Image analysis (computer-assisted), Monte Carlo simulation, Process control, spatio-temporal analysis
- in
- Quality and Reliability Engineering International
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:105030057050
- ISSN
- 0748-8017
- DOI
- 10.1002/qre.70184
- language
- English
- LU publication?
- yes
- id
- 81ad7b4b-dd59-4ecb-b371-1e53d7d9262e
- date added to LUP
- 2026-02-13 16:33:00
- date last changed
- 2026-04-17 04:00:47
@article{81ad7b4b-dd59-4ecb-b371-1e53d7d9262e,
abstract = {{Convolutional Neural Networks (CNNs) have emerged as one of the most effective tools for image analysis. In this study, we propose a custom-designed CNN architecture to construct a process control scheme based on image data. The product image is partitioned into equal-sized grids, each comprising three channels: i: reference image, ii: shifted image, and iii: their difference, which are individually input into the CNN. To train the model, we generate synthetic datasets representing both in-control and out-of-control conditions, tailored to reflect the specific nature of the monitoring task. The proposed method offers dual capabilities: it not only detects multiple simultaneous faults in different regions of the image but also localizes the positions of these faults; all in a single step. Performance evaluation is conducted using run length metrics for detection effectiveness and the Dice score for fault localization accuracy. Extensive simulation studies are carried out to assess the scheme’s performance under various shift magnitudes and spatial configurations. Comparative analysis with recently developed control schemes demonstrates the superior performance of our approach in detection, localization, or both across numerous scenarios. Finally, we provide a practical case study to illustrate the implementation of the proposed method in a real practice.}},
author = {{Sabahno, Hamed}},
issn = {{0748-8017}},
keywords = {{Convolutional neural networks (CNN); Image analysis (computer-assisted); Monte Carlo simulation; Process control; spatio-temporal analysis}},
language = {{eng}},
publisher = {{John Wiley & Sons Inc.}},
series = {{Quality and Reliability Engineering International}},
title = {{A CNN Approach for Simultaneous Spatiotemporal Fault Interpretation}},
url = {{https://lup.lub.lu.se/search/files/242259085/Quality_Reliability_Eng_-_2026_-_Sabahno_-_A_CNN_Approach_for_Simultaneous_Spatiotemporal_Fault_Interpretation.pdf}},
doi = {{10.1002/qre.70184}},
year = {{2026}},
}