关键词: 动态过程, 质量异常模式, 模式识别, 小波变换, 形状特征, 支持向量机, 粒子群优化

Abstract: To improve the monitoring efficiency for abnormal patterns in dynamic process,a novel quality abnormal pattern recognition model based on multi-features hybrid with Multi-class Support Vector Machine(MSVM)was proposed.The low frequency approximation coefficients and the wavelet reconstruction data were extracted from original data by discrete wavelet transform.The shape features extracted from wavelet reconstruction data were combined with the low frequency approximation coefficients to form the hybrid feature of quality pattern.The quality abnormal pattern was recognized by multi-class support vector machine optimized particle swarm optimization.Compared with the recognition model with a single class of feature and fusion feature,the simulation results illustrated that the recognition accuracy and training time of the proposed model had a remarkable improvement.

Key words: dynamic process, quality abnormal pattern, pattern recognition, wavelet transform, shape features, support vector machine, particle swarm optimization