Trade-offs in Metric Learning for Bearing Fault Diagnosis

Abstract

Metric learning is a well-developed field in machine learning and has seen recent application in the area of prognostics and health management (PHM). Metric learning allows for fault diagnosis or condition monitoring models to be developed with the assumption that a machine- or load-specific similarity metric can be learned after model deployment. Existing literature has used metric learning to fine-tune deep learning models to address machine-to-machine differences and differences in working conditions. Here, we study metric learning in isolation, not as an intermediate step in deep learning, by conducting a comparative study of Principal Component Analysis (PCA), Neighborhood Component Analysis (NCA), Local Fisher Discriminant Analysis (LFDA), and Large Margin Nearest Neighbor (LMNN). We consider performance metrics for prediction performance, cluster performance, feature sensitivity, sample efficiency, and latent space efficiency. We find that linear partitions on the latent spaces learned via metric learning are able to achieve accuracies greater than 90% on Case Western Reserve University’s bearing fault data set using only the drive-end vibration signal. We find PCA to be dominated by metric learning algorithms for all working loads considered. And, in sum, we demonstrate classical metric learning algorithms to be a promising approach for learning machine-and load-specific similarity metrics for PHM with minor data processing and small samples.