一种基于改进KICA的非高斯过程故障检测方法

Non-Gaussian process fault detection method based on modified KICA

针对基于核独立元分析(kernel independent component analysis,KICA)的故障检测方法只考虑非高斯信息提取而忽略局部近邻结构保持的问题,提出基于改进KICA的过程故障检测方法。将KICA法中只考虑非高斯信息提取的负熵最大化准则转换为熵最小化准则,结合局部保持投影的相似局部近邻结构准则,提出了同时考虑非高斯信息提取和局部近邻结构保持的目标函数,通过粒子群优化算法进行全局寻优,然后建立监控统计量对过程进行监控。在Tennessee Eastman过程上的仿真结果说明,与基于KICA的故障检测方法相比,所提方法能够在保持数据集局部近邻结构的同时,提取非高斯信息,能够有效缩短故障检测的延迟时间,提高故障检测率。

Many fault detection methods based on kernel independent component analysis（KICA）only consider the extraction of non-Gaussian information,but the preservation of local neighborhood structure is usually ignored.Aiming at this problem,a fault detection method based on modified KICA is proposed.The criterion of negentropy maximization in KICA considering only the extraction of non-Gaussian information is converted to the criterion of entropy minimization.The criterion of entropy minimization is then combined with the criterion of similar local neighborhood structure in locality preserving projections（LPP）,and a new objective function taking both the extraction of non-Gaussian information and the preservation of local neighborhood structure into account is constructed.The particle swarm optimization algorithm（PSO）is utilized to optimize the objective function globally,and the monitoring statistics are built to monitor the process.The simulation results on Tennessee Eastman process illustrate that,in contrast to fault detection methods based on KICA,the proposed method can extract the non-Gaussian information of dataset while keeping the local neighborhood structure,shorten the fault detection latency and improve the fault detection rate effectively.