Semi-supervised label consistent dictionary learning for machine fault classification

In this paper, we mainly present a Semi-Supervised Label Consistent KSVD (S²KSVD) algorithm for representing and classifying machine faults. The formulation of our S²KSVD is an improvement to the recent label consistent K-SVD (LC-KSVD), because LC-KSVD is a fully supervised approach, and needs to use supervised class information of all training data to compute a reconstructive & discriminative dictionary. But labeled signals are often expensive to obtain, while in contrast unlabeled signals can be easily captured with low expense from the real world. Thus, the application of LC-KSVD may be constrained in reality. To address this problem, we present S²KSVD through involving a computationally efficient label propagation (LP) process as a preprocessing step. The core idea is to employ the LP process to estimate the labels of unlabeled signals so that supervised prior knowledge that can significantly enhance classification can be increased. Simulation results on several machine fault datasets demonstrate that our algorithm delivers promising performance for machine fault classification.