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Open AccessArticle

VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring

Department of Smart Factory Convergence, Sungkyunkwan University, 2066 Seobu-ro, Jangan-gu, Suwon 16419, Korea
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This paper is an extended version of our paper published in Oh, C.; Moon, J.; Jeong, J. Explainable Process Monitoring based on Class Activation Map: garbage in, garbage out. In Proceedings of the ECML/PKDD 2020 Workshop on IoT Stream for Data Driven Predictive Maintenance, Ghent, Belgium, 18 September 2020.
Sensors 2020, 20(23), 6858; https://doi.org/10.3390/s20236858
Received: 31 October 2020 / Revised: 22 November 2020 / Accepted: 28 November 2020 / Published: 30 November 2020
Process monitoring at industrial sites contributes to system stability by detecting and diagnosing unexpected changes in a system. Today, as the infrastructure of industrial sites is advancing because of the development of communication technology, vast amounts of data are generated, and the importance of a way to effectively monitor such data in order to diagnose a system is increasing daily. Because a method based on a deep neural network can effectively extract information from a large amount of data, methods have been proposed to monitor processes using such networks to detect system faults and abnormalities. Neural-network-based process monitoring is effective in detecting faults, but has difficulty in diagnosing because of the limitations of the black-box model. Therefore, in this paper we propose a process-monitoring framework that can detect and diagnose faults. The proposed method uses a class activation map that results from diagnosis of faults and abnormalities, and verifies the diagnosis by post-processing the class activation map. This improves the detection of faults and abnormalities and generates a class activation map that provides a more verified diagnosis to the end user. In order to evaluate the performance of the proposed method, we did a simulation using publicly available industrial motor datasets. In addition, after establishing a system that can apply the proposed method to actual manufacturing companies that produce sapphire nozzles, we carried out a case study on whether fault detection and diagnosis were possible. View Full-Text
Keywords: class activation map; deep neural network; statistical process control; fault detection and diagnosis; anomaly detection class activation map; deep neural network; statistical process control; fault detection and diagnosis; anomaly detection
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MDPI and ACS Style

Oh, C.; Jeong, J. VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring. Sensors 2020, 20, 6858. https://doi.org/10.3390/s20236858

AMA Style

Oh C, Jeong J. VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring. Sensors. 2020; 20(23):6858. https://doi.org/10.3390/s20236858

Chicago/Turabian Style

Oh, Cheolhwan; Jeong, Jongpil. 2020. "VODCA: Verification of Diagnosis Using CAM-Based Approach for Explainable Process Monitoring" Sensors 20, no. 23: 6858. https://doi.org/10.3390/s20236858

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