밀도기반 군집화와 딥러닝을 활용한공정 주기 신호의 이상 탐지 및 분류Fault Detection and Classification of Process Cycle Signals Using Density-based Clustering and Deep Learning
- Other Titles
- Fault Detection and Classification of Process Cycle Signals Using Density-based Clustering and Deep Learning
- Authors
- 권상현; 안민정; 이홍철
- Issue Date
- 2018
- Publisher
- 대한산업공학회
- Keywords
- Fault Detection and Classification; Pattern Recognition; LSTM Autoencoder; Bidirectional LSTM; Density-Based Spatial Clustering of Application with Noise
- Citation
- 대한산업공학회지, v.44, no.6, pp.475 - 482
- Indexed
- KCI
- Journal Title
- 대한산업공학회지
- Volume
- 44
- Number
- 6
- Start Page
- 475
- End Page
- 482
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/79608
- DOI
- 10.7232/JKIIE.2018.44.6.475
- ISSN
- 1225-0988
- Abstract
- Process Fault Detection and Classification (FDC) distinguishes between normal and abnormal process cyclesignals. In the case of process cycle signals, quality control is difficult due to lack of information on patterns anddata imbalance. In this paper, We proposed a method to extract key features of cycle signal data by using LSTMAutoencoder and performed DBSCAN clustering to obtain information on patterns when there is no informationon process cycle signals. We used data augmentation especially when cluster with low density to eliminate thedata imbalance of the process signal. Through the above process, We finally constructed a bidirectional LSTMmodel for real-time process cycle signal classification. This provides a basis for smart factories by suggestingways to actively respond without relying on domain knowledge.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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