基于IMDS-DLNS方法的工业过程故障检测

针对多维尺度变换(multidimensional scaling,MDS)方法对高维数据进行维数约简时，新样本缺少映射矩阵无法进行低维嵌入的问题，提出了增量式多维尺度变换(incremental multidimensional scaling，IMDS)方法。首先，引入双重局部近邻标准化(dual local nearest neighbor standardization，DLNS）技术以解决IMDS方法降维后数据仍然具有多中心、方差差异明显等问题；其次，采用Hotelling T²统计量对过程进行监控，组成增量式多维尺度变换和双重局部近邻标准化的故障检测方法(IMDS-DLNS)；最后，通过数值模拟过程和青霉素发酵过程，将IMDS-DLNS方法分别与PCA，KPCA和FD-KNN等方法作对比分析。结果表明，IMDS-DLNS对比其他方法有更高的故障检测率。IMDS-DLNS方法对多变量、多模态过程具有良好的故障检测能力，能够保障产品质量和生产的安全性，可为工业过程故障检测研究提供参考。

Industrial process fault detection based on IMDS-DLNS method

Aiming at the problem that when the multidimensional scaling (MDS) method is used to reduce the dimensionality of high-dimensional data,the new sample lacks the mapping matrix and cannot carry out low-dimensional embedding,an incremental multidimensional scaling (IMDS) method was proposed.Firstly,the dual local nearest neighbor standardization (DLNS) technology was introduced to solve the problem of data having multiple centers and obvious variance differences after IMDS dimensionality reduction.Secondly,Hotelling T² statistics was used to monitor the process,and a fault detection method (IMDS-DLNS) with incremental multi-dimensional scale transformation and double local neighbor standardization was constructed.Finally,through numerical simulation of the process and penicillin fermentation process,the IMDS-DLNS method is compared with PCA,KPCA,FD-KNN and other methods,respectively.The results show that IMDS-DLNS has a higher fault detection rate compared to other methods.IMDS-DLNS method has good fault detection capabilities for multivariable and multimodal processes,and can guarantee product quality and production safety,which provides some reference for industrial process fault detection.