  In industrial applications, the early detection of malfunctioning factory
machinery is crucial. In this paper, we consider acoustic malfunction detection
via transfer learning. Contrary to the majority of current approaches which are
based on deep autoencoders, we propose to extract features using neural
networks that were pretrained on the task of image classification. We then use
these features to train a variety of anomaly detection models and show that
this improves results compared to convolutional autoencoders in recordings of
four different factory machines in noisy environments. Moreover, we find that
features extracted from ResNet based networks yield better results than those
from AlexNet and Squeezenet. In our setting, Gaussian Mixture Models and
One-Class Support Vector Machines achieve the best anomaly detection
performance.
