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Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound

1
Department of Digital Information Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea
2
Division of Computer & Electronic System Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(5), 1308; https://doi.org/10.3390/s18051308
Received: 2 March 2018 / Revised: 17 April 2018 / Accepted: 20 April 2018 / Published: 24 April 2018
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning in Sensors Networks)
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Abstract

Detecting an anomaly or an abnormal situation from given noise is highly useful in an environment where constantly verifying and monitoring a machine is required. As deep learning algorithms are further developed, current studies have focused on this problem. However, there are too many variables to define anomalies, and the human annotation for a large collection of abnormal data labeled at the class-level is very labor-intensive. In this paper, we propose to detect abnormal operation sounds or outliers in a very complex machine along with reducing the data-driven annotation cost. The architecture of the proposed model is based on an auto-encoder, and it uses the residual error, which stands for its reconstruction quality, to identify the anomaly. We assess our model using Surface-Mounted Device (SMD) machine sound, which is very complex, as experimental data, and state-of-the-art performance is successfully achieved for anomaly detection. View Full-Text
Keywords: auto-encoder; machine sound; anomaly detection; SMD; unsupervised learning auto-encoder; machine sound; anomaly detection; SMD; unsupervised learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Oh, D.Y.; Yun, I.D. Residual Error Based Anomaly Detection Using Auto-Encoder in SMD Machine Sound. Sensors 2018, 18, 1308.

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