A casting mold breakout prediction method based on feature vectors and hierarchical clustering. According to the present prediction method, temperature feature vectors of sticking breakout, past data under normal working conditions, and online real-time measured data are extracted to establish a sample set of feature vectors; the sample set is normalized and subject to hierarchical clustering; and then, whether the feature vectors extracted online falls within the breakout cluster is checked and determined, so as to identify and predict casting mold breakout. The prediction method avoids the tedious debugging and modification of parameters such as an alarm threshold, overcomes the artificial dependence of previous breakout prediction methods, and has good robustness and migration. By means of temperature feature extraction, not only can the temperature mode of sticking breakout be accurately recognized, false alarms can be avoided and the number of false alarms can be significantly reduced, the amount of data calculation and calculation time can be greatly reduced, thereby ensuring the instantaneity of online prediction.
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