基于TGNPE算法的间歇过程故障诊断

间歇过程数据是由批次、变量和时间构成的三维数据,数据内包含了丰富的对过程监控有用的全局和局部结构信息,如何充分提取间歇过程的特征信息是故障诊断的关键。传统方法处理三维数据都是将其展开成二维数据,展开过程必然会导致数据内在结构破坏,并且通常只考虑了数据的全局信息或者只考虑了数据的局部信息,这就不能充分提取过程的有用信息导致诊断效果欠佳。针对以上问题,提出了张量全局-局部邻域保持嵌入(TGNPE)算法,首先用张量分解的方法直接对三维数据进行建模,而不对数据进行展开,这就有效地保存了数据的内部结构,再用邻域保持嵌入算法充分提取数据局部结构信息的同时兼顾数据的全局信息,这就实现了对数据特征信息更加充分地提取,用TGNPE算法检测到故障后用贡献图法诊断出故障变量。通过青霉素发酵过程验证了本文提出的算法对间歇过程数据信息提取更加充分,更利于故障诊断。

Batch process fault diagnosis based on TGNPE algorithm

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.