Tensor dynamic neighborhood preserving embedding algorithm for fault diagnosis of batch process

Abstract

Batch process data has close relation in time series and dynamic characteristics. Traditional diagnosis algorithms often ignore process correlation of time series and dynamic characteristics, which would lead to larger errors of monitoring results. Aiming at dynamic characteristics of batch process, a tensor dynamic neighborhood preserving embedded (TDNPE) algorithm is proposed in this paper. Firstly, batch process data is regarded as a kind of second order tensor. The tensor factorization method is used to model that can avoid destroying internal structures of data. Then dynamic neighborhood preserving embedded algorithm is used to extract process feature information by considering local features of space and time in tensor space and that can effectively deal with process dynamic characteristics. The contribution plot method is used to diagnose fault variables when faults are detected. The simulation results of penicillin fermentation process verified the effectiveness of the proposed algorithm.