LAUSR

It has become a challenge to identify the discriminant information and the local geometric feature from the complex process data for improving fault diagnosis accuracy. Facing this challenge, this article proposes a novel fault diagnosis methodology based on discriminant neighborhood preserving embedding integrated with orthogonal locality preserving projections (DNPEOLPP). In DNPEOLPP, a novel local discriminant weight is constructed to identify the local geometric feature for dimensionality reduction (DR), so that the label of data is fully utilized. Furthermore, the cosine distance considering the spatial features of data is introduced into the process of neighbor graph construction to extract representative features. The bootstrap-based DNPEOLPP (B-DNPEOLPP) is eventually developed to further address the issue of singular matrix caused by the small number of classes when applying DNPEOLPP for DR. In addition, the Akaike information criterion (AIC) is utilized to identify the suitable order of DR, and AdaBoost.M2 is utilized to classify the type of fault. Finally, the Tennessee Eastman process (TEP) and the grid-connected photovoltaic system (GPVS) are used to validate the performance of B-DNPEOLPP. Through case study analysis, it is demonstrated that B-DNPEOLPP can effectively identify different faults and obtain superior accuracy in fault diagnosis.