{
  "id": "1907.11778",
  "version": "v1",
  "published": "2019-07-26T20:03:26.000Z",
  "updated": "2019-07-26T20:03:26.000Z",
  "title": "An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing",
  "authors": [
    "Baihong Jin",
    "Yingshui Tan",
    "Alexander Nettekoven",
    "Yuxin Chen",
    "Ufuk Topcu",
    "Yisong Yue",
    "Alberto Sangiovanni Vincentelli"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-07-26T20:03:26.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "anomaly detection",
      "encoder-decoder",
      "additive manufacturing",
      "application",
      "dataset contains images"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}