OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive am...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 21; H. 14; S. 4805
Hauptverfasser: Abbasi, Saad, Famouri, Mahmoud, Shafiee, Mohammad Javad, Wong, Alexander
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 14.07.2021
MDPI
Schlagworte:
Accuracy
acoustic anomaly detection
Acoustics
Communication
Datasets
Deep learning
Design
embedded machine learning
Machine learning
Neural networks
Receivers & amplifiers
Sound
Support vector machines
unsupervised learning
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30× lower latency than published networks.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21144805