现代工业过程通常具有规模大、流程长和工序多的特点,导致传统的集中式建模方法会淹没过程的局部变化信息,从而无法及时识别早期的非优运行状态.此外,闭环控制的广泛应用使得过程变量普遍存在时序相关性.针对以上问题,提出一种基于慢特征分析(Slow feature analysis,SFA)的分布式动态工业过程运行状态评价方法.首先,结合动态时间规整(Dynamic time warping,DTW)和K-medoids聚类算法对过程进行分解;然后,对每一变量子块建立相应的动态慢特征分析(Dynamic slow feature analysis,DSFA)模型;最后,利用贝叶斯推理获得全局的综合评价指标.通过在数值案例和金湿法冶金过程的仿真应用,验证了该方法的有效性.
Abstract
The modern industrial processes are generally characterized by large scale,long processes and multiple procedures.In this case,the traditional centralized model may submerge the local change information of the pro-cesses,thus failing to identify the early non-optimal operation status in time.In addition,the wide application of closed-loop control brings the universal existence of temporal correlations of process variables.In view of the above problem,a distributed operating performance assessment scheme of dynamic industrial processes based on slow fea-ture analysis(SFA)is proposed.First,the process decomposition is realized by combining dynamic time warping(DTW)and K-medoids clustering algorithms.Second,the corresponding dynamic slow feature analysis(DSFA)model is established for each sub-block.Finally,the overall comprehensive assessment index is obtained through Bayesian inference.The effectiveness of the scheme is verified by numerical examples and gold hydrometallurgy pro-cess.
关键词
分布式模型/运行状态评价/慢特征分析/动态时间规整/K-medoids聚类
Key words
Distributed model/operating performance assessment/slow feature analysis(SFA)/dynamic time warp-ing(DTW)/K-medoids clustering
维普
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