[3Pin1] インタラクティブ(1)

[3Pin1-44] Anomaly Detection in Control Valves by 1d CNN-LSTM

〇Takahiro Motegi¹, Yuya Nakazawa¹, Tetsuya Tabaru¹ (1. Azbil Corporation)

Keywords:Anomaly Detection, 1d CNNs, LSTM

This paper proposes an anomaly detection system for time series data by using 1d CNN-LSTM networks, which is a combination of one dimensional convolutional neural network (1d CNN) and long short time memory (LSTM). The CNN processes a time series data from a sensor and its output is passed to LSTM. After training of the networks, the CNN grows into an appropriate filter to emphasize features of the time series in frequency domain, and the LSTM becomes a good feature extractor. The extractor works fine even under noisy conditions. It generates a feature vector and the vector is utilized to diagnose anomalies. We applied the proposed system to vibration sensor data obtained from a control valve. The system detected anomalies due to cavitation, which is one of the most serious phenomenon of control valves, with 99.5% accuracy. The result shows availability of our proposed method.