基于改进偏最小二乘法的多模态过程故障检测方法

针对传统的数据驱动方法偏最小二乘法（PLS）中存在的多模态数据故障检测效果不佳的问题，提出了一种新的故障检测方法——基于局部近邻标准化（LNS）的PLS（LNS-PLS）。首先，利用LNS方法对原始数据进行高斯化处理，在此基础上建立PLS的监控模型，确定T²和平方预测误差（SPE）的控制限；其次，对测试数据同样进行LNS标准化处理，再计算出测试数据的PLS监控指标来进行过程监视及故障检测，解决了PLS中无法处理多模态的问题。将所提方法应用于数值例子和青霉素生产过程，并将其测试结果与主成分分析（PCA）、K最近邻（KNN）、PLS等方法进行对比分析。实验结果表明，所提方法的故障检测效果优于PLS、KNN、PCA，该方法在分类及多模态过程故障检测方面有较高的准确性。

Multi-modal process fault detection method based on improved partial least squares

Partial Least Squares (PLS) as the traditional data-driven method has the problem of poor performance of multi-modal data fault detection. In order to solve the problem, a new fault detection method was proposed, which called PLS based on Local Neighborhood Standardization (LNS) (LNS-PLS). Firstly, the original data was Gaussized by LNS method. On this basis, the monitoring model of PLS was established, and the control limits of T² and Squared Prediction Error (SPE) were determined. Secondly, the test data was also standardized by the LNS, and then the PLS monitoring indicators of test data were calculated for process monitoring and fault detection, which solved the problem of unable to deal with multi-modal by PLS. The proposed method was applied to numerical examples and penicillin production process, and its test results were compared with those of Principal Component Analysis (PCA), K Nearest Neighbors (KNN) and PLS. The experimental results show that, the proposed method is superior to PLS, KNN and PCA in fault detection. The proposed method has high accuracy in classification and multi-modal process fault detection.