CAJ | 학술논문

为了减少计算量和信息丢失，提出一种运用多线性主元分析（ Multilinear PCA ）进行间歇过程故障诊断的新方法。首先运用Multilinear PCA直接对间歇过程三维数据进行降维，得到低维的投影向量。然后所有批次向投影向量上投影得到得分向量，计算SPE统计指标控制限，建立Multilinear PCA模型。建立Multilinear PCA模型后，计算新批次的得分向量和SPE（ Squared Prediction Error）统计指标，根据统计指标是否超限监视生产过程的运行。最后，在检测出故障之后，采用SPE贡献图诊断故障原因。仿真实例表明：与多向主元分析法MPCA相比（ Muhiway Principal Component Analysis ）。 Multilinear PCA提高了过程性能监视和故障诊断的准确性，较早地发现过程异常。
위료감소계산량화신식주실，제출일충운용다선성주원분석（ Multilinear PCA ）진행간헐과정고장진단적신방법。수선운용Multilinear PCA직접대간헐과정삼유수거진행강유，득도저유적투영향량。연후소유비차향투영향량상투영득도득분향량，계산SPE통계지표공제한，건립Multilinear PCA모형。건립Multilinear PCA모형후，계산신비차적득분향량화SPE（ Squared Prediction Error）통계지표，근거통계지표시부초한감시생산과정적운행。최후，재검측출고장지후，채용SPE공헌도진단고장원인。방진실례표명：여다향주원분석법MPCA상비（ Muhiway Principal Component Analysis ）。 Multilinear PCA제고료과정성능감시화고장진단적준학성，교조지발현과정이상。
In order to decrease the computation complexity and information loss ,in this paper we propose a new method to diagnose the in-termittent process fault with multilinear principal component analysis (multilinear PCA).First,we apply the multilinear PCA directly to the di-mension reduction of three-dimensional data of intermittent process and get the projection vectors with low dimensions .Then all the batches are projected onto the projection vectors to get the scoring vectors ,and we calculate the control limits of SPE statistics indexes and build the multi-linear PCA model.Thirdly,we calculate the scoring vectors and SPE statistics indexes of the new batch ,and monitor the production operation according to whether the statistical index exceeding the control limit .Finally,we adopt SPE contribution chart to diagnose the fault cause when the faults are detected.Simulation examples show that compared with multiway principal component analysis (MPCA),the multilinear PCA improves the accuracy of process performance monitoring and fault diagnosis ,and finds the abnormal process earlier .