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Quality-Discriminative Localization of Multisensor Signals for Root Cause Analysis

Authors
Cho, Yoon SangKim, Seoung Bum
Issue Date
7월-2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Feature extraction; Object oriented modeling; Location awareness; Manufacturing processes; Predictive models; Quality assessment; Product design; Activation mapping; convolutional neural network (CNN); multisensor signal data; quality-discriminative localization; root cause analysis (RCA); steel manufacturing
Citation
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, v.52, no.7, pp.4374 - 4387
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
Volume
52
Number
7
Start Page
4374
End Page
4387
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/145874
DOI
10.1109/TSMC.2021.3096529
ISSN
2168-2216
Abstract
Root cause analysis (RCA) methods for effectively identifying the critical causes of abnormal processes have attracted attention because manufacturing processes have grown in scale and complexity. However, the existing methods for building automatic RCA models suffer from the disadvantage of typically requiring expert knowledge. In addition, without a dataset representing the causal relationship of multivariate processes, it is difficult to provide useful information for RCA. Although data-driven RCA methods have been proposed, most are based on classification models. Given that product quality is defined as a continuous variable in many manufacturing industries, classification models are limited in deriving root causes affecting the product quality level. In this article, we propose a regression model-based RCA method, which we call quality-discriminative localization, consisting of a convolutional neural network (CNN)-based activation mapping of multisensor signal data. In our proposed method, the CNN predicts the product quality of a continuous variable. Activation mapping then extracts causal maps that highlight significant sensor signals for each product. To identify the root causes, we generate a root cause map from the weighted sum of quality and causal maps. We consider root causes as locations of abnormal processes and processing times from localized activation scores on the root cause map. We experimentally demonstrate the usefulness of the proposed method with simulated data and real process data from a steel manufacturing process. Our results show that the proposed method successfully identifies root causes with distinct sensor signal patterns.
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