中文摘要：

针对多向核熵偏最小二乘（multi-way kernel entropy partial least squares,MKEPLS）利用的是数据的一阶和二阶统计特性未考虑数据的高阶统计特性,在进行特征提取时会造成有用数据丢失的问题,提出基于高阶统计量的多向核熵偏最小二乘方法（higher order statistics multi-way kernel entropy partial least squares,HOS-MKEPLS）.首先,通过构造样本的高阶统计量将数据从原始的数据空间映射到高阶统计量样本空间.然后,再建立MKEPLS监控模型进行质量相关的故障监控,当监控到有故障发生时进行故障变量的追溯.最后,将该方法应用到工业青霉素发酵过程的监测中并与MKEPLS进行比较.结果表明：该方法具有更好的监控和故障识别性能.

英文摘要：

As the multi-way kernel entropy partial least squares（MKEPLS） method does not make full use of the higher-order statistics of the process data,which will lose the important information in the feature extraction,and result in degraded fault identification performance.To solve this problem,a novel method based on higher order statistics and multi-way kernel entropy partial least squares（HOSMKEPLS） is proposed,in which the raw data space is projected into statistics space by calculating the higher order statistics of the data set,establishing the monitoring MKEPLS model,then adopting the contribution figure method on the trace of the fault variables.Finallay,the method is applied to an industrial penicillin fermentation process,and compared with the MKEPLS model.Results show that the method has a better monitoring performance and can detect and identify the fault.