基于自适应混合核典型变量分析的工业过程质量相关故障检测

质量相关故障检测技术是保障工业过程安全顺行和质量稳定的重要手段,是当前流程工业过程控制领域的研究热点.针对工业过程的非线性与动态特性及其质量相关故障的时变特性,提出一种基于自适应混合核典型变量分析(AMKCVA)的质量相关故障检测方法.该方法通过设计合理的混合核函数和自适应监测统计量,提升了工业过程质量相关故障的检测性能.通过对典型的热轧过程现场数据进行仿真验证,并与传统方法对比分析,表明了所提算法的有效性和实用性.

Quality-related fault detection for industrial processes based on adaptive mixed kernel canonical variable analysis

Quality-related fault detection technology is an important mean to ensure safe operation and stable quality for industrial processes, which, thus, has recently become hotspots in the process industrial control domain. In this paper, an quality-related fault detection method based on adaptive mixed kernel canonical variable analysis(AMKCVA) is developed, considering the nonlinear and dynamic characteristics of industrial processes, as well as the varying characteristic of quality-related faults. Under that framework, mixed kernel functions and adaptive monitoring statistics are reasonably designed for improving the performance of quality-related fault detection. Finally, a case study on hot rolling process is given to demonstrate the advantages of the proposed approach compared with other methods.