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U.S. Department of Energy
Office of Scientific and Technical Information
 

Deep anomaly detection for industrial systems: a case study

Conference ·
 [1]; ; ; ;
  1. GE Research
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We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation are used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are discrete in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.
Research Organization:
GE Research
Sponsoring Organization:
USDOE Office of Fossil Energy (FE)
DOE Contract Number:
FE0031763
OSTI ID:
1717878
Report Number(s):
DOE-GR-FE0031763-1
Country of Publication:
United States
Language:
English

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Related Subjects

anomaly detection
deep neural network