2018 年 54 巻 7 号 p. 599-605
A vacuum pump is always using in semiconductor manufacturing equipment. Anomaly of a vacuum pump leads to troubles such as stoppage of equipment. Therefore, anomaly detection of a vacuum pump is very important. We considered a vacuum pump state estimation using thin AE (Acoustic Emission) sensor and machine learning. However, anomaly data is not always available in a production line. Anomaly detector is necessary to learn from only normal data. There is an anomaly detection using autoencoder in recent years. In autoencoder, normal data is reconstructed and anomaly data is not reconstructed. As a result, anomaly detection can using reconstruction error. In this paper, we propose anomaly detection for rotary vacuum pump by thin AE sensor and reconstruction error of autoencoder. Performance of the proposed method is evaluated by detect exhaust anomaly of a vacuum pump.