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Anomaly detection is important for industrial automation and part qualityassurance, and while humans can easily detect anomalies in components given afew examples, designing a generic automated system that can perform at human orabove human capabilities remains a challenge. In this work, we present a simplenew anomaly detection algorithm called FADS (feature-based anomaly detectionsystem) which leverages pretrained convolutional neural networks (CNN) togenerate a statistical model of nominal inputs by observing the activation ofthe convolutional filters. During inference the system compares theconvolutional filter activation of the new input to the statistical model andflags activations that are outside the expected range of values and thereforelikely an anomaly. By using a pretrained network, FADS demonstrates excellentperformance similar to or better than other machine learning approaches toanomaly detection while at the same time FADS requires no tuning of the CNNweights. We demonstrate FADS ability by detecting process parameter changes ona custom dataset of additively manufactured lattices. The FADS localizationalgorithm shows that textural differences that are visible on the surface canbe used to detect process parameter changes. In addition, we test FADS onbenchmark datasets, such as the MVTec Anomaly Detection dataset, and reportgood results.

Feature anomaly detection system (FADS) for intelligent manufacturing