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中文摘要：
多向独立成分分析（MICA）能够获取信号的高阶统计量信息,在间歇过程故障监测中得到长足发展。针对Fast ICA算法提取非高斯独立成分时,易受初始点的影响,梯度下降无法收敛到极小点以及算法运行前独立主元个数未知的不足,提出基于粒子群优化的MICA算法。并引入支持向量数据描述（SVDD）算法确定过程监控统计量的置信限,避免了核密度估计带来的＂维数灾难＂等问题。实验设计由青霉素发酵仿真平台完成,结果显示了本文方法优越于传统MICA方法,能够保证获取非高斯性最大的独立成分,使得对故障的监测更加及时有效。
英文摘要：
Multi-way Independent Component Analysis can obtain higher order statistics of the signal,which has gotten great progress for fault detection of batch processes. Fast ICA algorithm easily affects by the initial point when solving non-gaussian independent ingredients,which cannot convergence to the minimum point and has no idea for the principal independent component number before running it. To solve the above mentioned problems,a particle swarm optimization based on MICA algorithm is proposed. Also,support vector data description method is introduced to determine the control limit of monitoring statistics,avoiding the ＂dimension disaster＂problem caused by kernel density estimation. Design of experiments has performed by penicillin fermentation simulation platform. The result shows that the proposed method is superior to traditional MICA,which can maximize the non-gaussian characteristic of the extracted independent components,and make fault detection more timely and effectively.
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