Joint Label Consistent Dictionary Learning and Adaptive Label Prediction for Semisupervised Machine Fault Classification

In this paper, we propose a semisupervised label consistent dictionary learning (SSDL) framework for machine fault classification. SSDL is a semisupervised extension of recent fully supervised label consistent dictionary learning approach, since the number of labeled machine data is usually limited in practice. To enable the supervised dictionary learning model to use both labeled and commonly readily available unlabeled data for enhancing performance, we propose to incorporate the merits of label prediction and present a joint label consistent dictionary learning and adaptive label prediction technique. In this setting, we first employ the existing label prediction model to estimate the labels of unlabeled training signals in a transductive fashion for enriching supervised prior. Then, we use predicted labeled data for label consistent dictionary learning. After that, we apply the discriminant sparse codes as the adaptive reconstruction weights for label prediction to update the estimated labels of unlabeled training data and the discriminative sparse codes matrix for label consistent dictionary learning so that classification performance can be enhanced. Thus, an informative dictionary, a sparse-code matrix, and an optimal multiclass classifier can be alternately obtained from one objective function. Besides, the tricky process of choosing optimal kernel width and neighborhood size can also be effectively voided in our scheme due to the adaptive weights. Extensive simulations on several machine fault datasets show that our SSDL method can deliver enhanced performance over other state-of-the-arts for machine fault classification.