Zero-fault-shot learning for bearing spall type classification by hybrid approach

Abstract

Spall type classification, a task in which the type of the spall is estimated, is an important stage for bearing diagnosis and prognosis. Many machine learning algorithms have been suggested for spall type classification. However, they are not relevant for diagnosing critical rotating machinery, where very few, if any, faulty examples (labeled and unlabeled) are available due to safety considerations. In this study, a novel hybrid algorithm is proposed, which enables the classification of the spall type based on zero-fault-shot learning. The novel algorithm combines physics-based algorithms together with machine learning to overcome the lack of faulty data. It projects the signals into an invariant feature space by physics-based algorithms and classifies the spall type by a fully connected neural network. The new algorithm is demonstrated on several well-known experimental datasets and significantly improves the performance of currently available learning algorithms for zero-fault-shot learning. It improves the results of the state-of-the-art algorithm from at most 60% on the six tested datasets to at least 98% accuracy on all tested datasets with the newly suggested algorithm.