Application of Time Series Data Anomaly Detection Based on Deep Learning in Continuous Casting Process

抄録

The inclusion is a crucial factor affecting the quality of cord steel. The formation of inclusions is closely related to the abnormal production process in continuous casting process. Automatic anomaly detection algorithms are proposed to replace manual visual screening according to the smart manufacturing paradigm, and then the relationship between abnormal production process and product quality is mined through data-driven methods in this paper. Convolutional neural networks and autoencoder models are employed to detect various types of anomalies in time-dependent process parameters. A new idea of detecting abnormal intervals from time series is implemented instead of the conventional process monitoring based on the univariate control limit in process specifications. The abnormal intervals including starting time, duration and type are detected. Furthermore, the proposed scheme progresses from univariate detection to multi-variable process monitoring, which considers the nonlinear coupling of the process. Finally, various anomaly detection results are fused to analyze whether inclusions exist in the cast slab. The proposed scheme is applied to the continuous casting process of cord steel. The automatic anomaly detection scheme is verified to be effective via plenty of actual production data, with the recall rate of 93.06%. It is of prominent significance for product quality improvement of the cord steel.