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We present a novel unsupervised deep learning approach that utilizes theencoder-decoder architecture for detecting anomalies in sequential sensor datacollected during industrial manufacturing. Our approach is designed not only todetect whether there exists an anomaly at a given time step, but also topredict what will happen next in the (sequential) process. We demonstrate ourapproach on a dataset collected from a real-world testbed. The dataset containsimages collected under both normal conditions and synthetic anomalies. We showthat the encoder-decoder model is able to identify the injected anomalies in amodern manufacturing process in an unsupervised fashion. In addition, it alsogives hints about the temperature non-uniformity of the testbed duringmanufacturing, which is what we are not aware of before doing the experiment.

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