Semi-supervised machinery health assessment framework via temporal broad learning system embedding manifold regularization with unlabeled data

Abstract

In the era of industrial big data, data-driven methods based on deep learning have made a splash in the field of mechanical PHM. However, the performance of deep learning models is limited by the depth of networks while previous supervised learning methods can only be applied to a small amount of labeled data while most of data acquired in the field are unlabeled, resulting in overfitting and low generalization of models. To address the above issues, a semi-supervised framework via temporal broad learning system embedding manifold regularization (TBLSMR) is proposed for machinery health assessment with unlabeled data. Specifically, linear and nonlinear temporal feature information is first learned from labeled and unlabeled data by TBLS. Then the Laplacian matrix is introduced embedding manifold regularization and used to construct objective function. Meanwhile, a specific ridge regression algorithm is designed to easily calculate the weights of network. Finally, an end-to-end mapping between the feature information layer and the label layer is established. The effectiveness of the proposed method is verified by two case studies. The comparison results with traditional models show that TBLSMR obtains high prediction accuracy while achieving training consumption savings, making it a promising approach for machinery health assessment that meets industrial needs.