Oh Dong Yul, Yun Il Dong
Department of Digital Information Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea.
Division of Computer & Electronic System Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea.
Sensors (Basel). 2018 Apr 24;18(5):1308. doi: 10.3390/s18051308.
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.
在需要持续对机器进行验证和监测的环境中，从给定噪声中检测异常或异常情况非常有用。随着深度学习算法的进一步发展，当前的研究已聚焦于这个问题。然而，定义异常的变量太多，并且对大量按类别标注的异常数据进行人工标注非常耗费人力。在本文中，我们提议在降低数据驱动的标注成本的同时，检测非常复杂机器中的异常运行声音或离群值。所提出模型的架构基于自动编码器，并使用代表其重建质量的残差误差来识别异常。我们使用非常复杂的表面贴装器件（SMD）机器声音作为实验数据来评估我们的模型，并在异常检测方面成功实现了领先的性能。
