A novel density ratio-based batch active learning fault diagnosis method integrated with adaptive Laplacian graph trimming

In actual industrial processes, although a large number of original data are easy to obtain, only a few samples are effectively labelled, which is insufficient to construct a supervised fault diagnostic model. Facing the industrial demand of fault diagnosis, in this paper, a novel density ratio (DR)-based batch active learning (BAL) fault diagnosis method integrated with adaptive Laplacian graph trimming (ALGT) method is proposed. First, under the active learning framework, a new index DR-based on local reachability density (LRD) is proposed to search the low density and high uncertainty samples, in which the local outliers factor (LOF) is used to search the samples in low density region and the ratio of LRD and intra-class LRD is calculated to search the samples with high uncertainty. Second, the samples are selected and manually labelled in batches according to the proposed index DR, and the labelled data set and the unlabelled data set are updated and reconstructed. Third, based on the reconstructed labelled dataset and remaining unlabelled dataset, a semi-supervised classifier ALGT is constructed for fault diagnosis. In ALGT, the Laplacian weighted graph is initialized and iteratively optimized by ALGT. Finally, the proposed DR-based BAL-ALGT (DRBAL-ALGT) fault diagnosis method is verified by the Tennessee Eastman process (TEP) and applied to grid-connected photovoltaic systems (GPVS). The experimental results show that the proposed DRBAL-ALGT method can achieve higher accuracy for fault diagnosis.