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arXiv · 1907.11778

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

Baihong Jin·Yingshui Tan·Alexander Nettekoven·Yuxin Chen·Ufuk Topcu·Yisong Yue·Alberto Sangiovanni Vincentelli
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Abstract

We present a novel unsupervised deep learning approach that utilizes the encoder-decoder architecture for detecting anomalies in sequential sensor data collected during industrial manufacturing. Our approach is designed not only to 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 testbed. The dataset contains images collected under both normal conditions and synthetic anomalies. We show that the encoder-decoder model is able to identify the injected anomalies in a modern manufacturing process in an unsupervised fashion. In addition, it also gives hints about the temperature non-uniformity of the testbed during manufacturing, which is what we are not aware of before doing the experiment.

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Baihong Jin, Yingshui Tan, Alexander Nettekoven, Yuxin Chen, Ufuk Topcu, Yisong Yue, Alberto Sangiovanni Vincentelli. 2019-07-26. An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing. https://arxiv.org/abs/1907.11778

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