基于支持向量机MPLS的间歇过程故障诊断方法

1 INTRODUCTION In batch or fed-batch processes, raw materials are converted to products within a finite duration. In prac- tical production, the process commonly exhibits large variations from batch to batch due to such influencing factors as the quality fluctuation of raw materials, de- fect of equipments, contaminations, and other unpre- dicted disturbances. These variations may have an adverse effect on the final product quantity and quality. But it is generally difficult to discern th…

On-line Fault Detection Using SVM-based Dynamic MPLS for Batch Processes

In this article, a nonlinear dynamic multiway partial least squares (MPLS) based on support vector machines (SVM) is developed for on-line fault detection in batch processes. The approach, referred to as SVM-based DMPLS, integrates the SVM with the MPLS model. Process data from normal historical batches are used to develop the MPLS model, and a series of single-input-single-output SVM networks are adopted to approximate nonlinear inner relationship between input and output variables. In addition, the application of a time-lagged window technique not only makes the complementarities of unmeasured data of the monitored batch unnecessary, but also significantly reduces the computation and storage requirements in comparison with the traditional MPLS. The proposed approach is validated by a simulation study of on-line fault detection for a fed-batch penicillin production.