CAJ | 학술논문

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。
침대다향핵적편최소이승( multi-way kernel entropy partial least squares, MKEPLS)이용적시수거적일계화이계통계특성미고필수거적고계통계특성,재진행특정제취시회조성유용수거주실적문제,제출기우고계통계량적다향핵적편최소이승방법( higher order statistics multi-way kernel entropy partial least squares, HOS-MKEPLS)。수선,통과구조양본적고계통계량장수거종원시적수거공간영사도고계통계량양본공간。연후,재건립MKEPLS감공모형진행질량상관적고장감공,당감공도유고장발생시진행고장변량적추소。최후,장해방법응용도공업청매소발효과정적감측중병여MKEPLS진행비교。결과표명：해방법구유경호적감공화고장식별성능。
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 ( HOS-MKEPLS) 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.