Integrated Approach to Density-Based Spatial Clustering of Applications with Noise and Dynamic Time Warping for Breakout Prediction in Slab Continuous Casting

Abstract

Mold breakout is a catastrophic accident that has serious impacts on smooth production, slab quality, and caster equipment. Accurate identification and prediction of an impending breakout are always top priorities in continuous casting operations. In view of crucial common features of mold copper plate temperatures during a breakout, such as time lag and space inversion, the concepts of density-based spatial clustering of applications with noise and dynamic time warping are introduced, and an integrated novel method for breakout prediction is developed. Through extracting and fusing the representative singularity and approximation of temperature variation, the typical temporal and spatial temperature characteristics during breakout can be distinguished and predicted accurately. Compared with traditional methods of logical judgment and artificial neural network, the method based on clustering does not need to modify forecast thresholds or parameters artificially, which overcomes the limitation of model dependence on human beings, and demonstrates excellent adaptability and robustness for online abnormality prevention.