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An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing

Jin, Baihong and Tan, Yingshui and Nettekoven, Alexander and Chen, Yuxin and Topcu, Ufuk and Yue, Yisong and Sangiovanni Vincentelli, Alberto (2019) An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing. In: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA). IEEE , Piscataway, NJ, pp. 1008-1015. ISBN 978-1-7281-4550-1. https://resolver.caltech.edu/CaltechAUTHORS:20190905-154317486

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Abstract

We present a novel unsupervised deep learning approach that utilizes an encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed to not only detect whether there exists an anomaly at a given time step, but also to predict what will happen next in the (sequential) process. We demonstrate our approach on a dataset collected from a real-world Additive Manufacturing (AM) testbed. The dataset contains infrared (IR) images collected under both normal conditions and synthetic anomalies. We show that our encoder-decoder model is able to identify the injected anomalies in a modern AM manufacturing process in an unsupervised fashion. In addition, our approach also gives hints about the temperature non-uniformity of the testbed during manufacturing, which was not previously known prior to the experiment.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1109/ICMLA.2019.00171DOIArticle
https://arxiv.org/abs/1907.11778arXivDiscussion Paper
ORCID:
AuthorORCID
Yue, Yisong0000-0001-9127-1989
Sangiovanni Vincentelli, Alberto0000-0003-1298-8389
Additional Information:© 2019 IEEE. This work is supported in part by the National Research Foundation of Singapore through a grant to the Berkeley Education Alliance for Research in Singapore (BEARS) for the Singapore-Berkeley Building Efficiency and Sustainability in the Tropics (SinBerBEST) program, and by the National Science Foundation under Grant No. 1645964 and 1646522. BEARS has been established by the University of California, Berkeley as a center for intellectual excellence in research and education in Singapore. Yuxin Chen is supported in part by a Swiss NSF Mobility Postdoctoral Fellowship and a PIMCO Fellowship. Baihong Jin and Yingshui Tan contributed equally to this paper. Alexander Nettekoven prepared and processed the experimental data. Dr. Yuxin Chen contributed to the theoretical aspects of this paper.
Funders:
Funding AgencyGrant Number
National Research Foundation (Singapore)UNSPECIFIED
NSFCNS-1645964
NSFCNS-1646522
Swiss National Science Foundation (SNSF)UNSPECIFIED
PIMCO FoundationUNSPECIFIED
Subject Keywords:additive manufacturing, machine learning, anomaly detection, fault detection and diagnosis
Record Number:CaltechAUTHORS:20190905-154317486
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190905-154317486
Official Citation:Y. Tan et al., "An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing," 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019, pp. 1008-1015. doi: 10.1109/ICMLA.2019.00171
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98461
Collection:CaltechAUTHORS
Deposited By: George Porter
Deposited On:05 Sep 2019 23:08
Last Modified:20 Feb 2020 22:31

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