关键词: 间歇过程, 故障诊断, 张量分解, 全局-局部邻域保持嵌入

Abstract:

Batch processes data is three dimensional data composed of lots of the batches, variables and time. The data contains abundant useful global and local structure information for process monitoring. The key of fault diagnosis is to fully extract the feature information of batch process. The traditional methods unfold three-dimensional data to two-dimensional data. The process inevitably leads to destructing internal structure of the data. The traditional methods usually only consider the global information data or local information data, then the useful process information can not be fully extracted, which leads to poor diagnosis. Aiming to above problems, a tensor global-local neighborhood preserving embedding (TGNPE) algorithm is proposed in this paper. First tensor factorization is used to deal with three-dimensional data directly which effectively save the internal structure of the data. Then the neighborhood preserving embedding algorithm is used to extract the local structure of the data information, at the same time considering the global information of the data. The data information can be fully extracted under keeping internal data structure. The contribution plot method is used to diagnose fault variables after detecting faults. The simulation results of penicillin fermentation process verified the effectiveness of the proposed algorithm.

Key words: batch process, fault diagnosis, tensor factorization, global-local neighborhood preserving embedding