Reconstruction based fault diagnosis using concurrent phase partition and analysis of relative changes for multiphase batch processes with limited fault batches

Abstract

For batch processes, if sufficient fault batches are available, fault characteristics can be well understood and extracted, providing important information for fault diagnosis. However, sometimes, it is difficult and may be impractical to get sufficient batches for every fault case. Thus, how to derive reliable fault information based on limited batches has been an important question for fault diagnosis which, however, has not been well addressed yet. Starting from limited fault batches, this article proposes a fault diagnosis strategy based on reconstruction technique for multiphase batch processes. Two important modeling procedures are implemented by making full use of limited fault batches, concurrent phase partition and analysis of relative changes. First, for each fault case, a generalized time-slice is constructed by combining several consecutive time-slices within a short time region to explore local process correlations. The time-varying characteristics of normal and fault statuses are then jointly analyzed so that multiple sequential phases are identified simultaneously for all fault cases and normal case. Then, in each phase, monitoring models are developed from normal case with sufficient batches and each fault case is related with normal case for relative analysis to explore the relative changes (i.e., the fault effects). Comprehensive subspace decomposition is implemented where alarm-responsible fault deviations are extracted and used to develop fault reconstruction models which can more efficiently recover fault-free part and identify fault cause. Starting from limited batches, the proposed algorithm can efficiently distinguish different fault cases and offer reliable fault diagnosis performance. It is illustrated with a typical multiphase batch process, including one normal case and three fault cases with limited batches.