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

Autor: Dong Yul Oh, Il Dong Yun
Jazyk: angličtina
Rok vydání: 2018
Předmět:
Zdroj: Sensors, Vol 18, Iss 5, p 1308 (2018)
Druh dokumentu: article
ISSN: 1424-8220
18051308
DOI: 10.3390/s18051308
Popis: 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.
Databáze: Directory of Open Access Journals
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