Industrial Anomaly Detection: A Comparison of Unsupervised Neural Network Architectures
Barry Siegel
Abstract
Anomaly detection is critical to the efficient and secure operation of industrial equipment, integrated sensors, and the overall production process. Cyber-physical systems and supervisory control and data acquisition systems often embed algorithms for anomaly detection. Anomalies are, by definition, rare events and labor-intensive to identify. Pipelined deep learning techniques such as recurrent neural networks (RNN), 1-D convolution neural networks (1DCNN), and generative adversarial networks create unique anomaly detection architectures. These architectures are unsupervised, and labeled anomaly data is not required. With two large, multivariate time-series testbed experimental datasets, comparisons are made between the neural network architectures and traditional machine learning (TML) baseline techniques. Evaluation metrics appropriate for imbalanced anomaly datasets include the area under the receiver operating characteristic curve and the area under the precision-recall tradeoff curve. Experimental results demonstrate that pipelined neural network architectures exhibit higher detection rates than the TML techniques. 1DCNN can substitute for RNNs when performance and long time-sequences are essential.