Department of Computer and Electronic Systems Engineering, Hankuk University of Foreign Studies, Yongin 17035, Korea.
Sensors (Basel). 2018 Oct 22;18(10):3573. doi: 10.3390/s18103573.
Surface Mounted Device (SMD) assembly machine manufactures various products on a flexible manufacturing line. An anomaly detection model that can adapt to the various manufacturing environments very fast is required. In this paper, we proposed a fast adaptive anomaly detection model based on a Recurrent Neural Network (RNN) Encoder⁻Decoder with operating machine sounds. RNN Encoder⁻Decoder has a structure very similar to Auto-Encoder (AE), but the former has significantly reduced parameters compared to the latter because of its rolled structure. Thus, the RNN Encoder⁻Decoder only requires a short training process for fast adaptation. The anomaly detection model decides abnormality based on Euclidean distance between generated sequences and observed sequence from machine sounds. Experimental evaluation was conducted on a set of dataset from the SMD assembly machine. Results showed cutting-edge performance with fast adaptation.
表面贴装设备 (SMD) 装配机在柔性制造线上制造各种产品。需要一种能够非常快速适应各种制造环境的异常检测模型。在本文中，我们提出了一种基于循环神经网络 (RNN) 编解码器的快速自适应异常检测模型，该模型使用机器声音进行操作。RNN 编解码器的结构与自动编码器 (AE) 非常相似，但由于其滚动结构，前者的参数明显减少。因此，RNN 编解码器只需要很短的训练过程即可快速适应。异常检测模型根据机器声音生成序列和观测序列之间的欧几里得距离来判断异常。实验评估是在 SMD 装配机的一组数据集上进行的。结果表明，该模型具有快速自适应的卓越性能。
