Multiscale Wavelet Prototypical Network for Cross-Component Few-Shot Intelligent Fault Diagnosis

The techniques of machine learning, as well as deep learning (DL) methods, have seen a wide application in the intelligent fault diagnosis field these years. However, contemporary methods are still restricted under some drawbacks: 1) conventional DL-based models always rely on the quality and amount of the data. However, there are usually insufficient samples in practical scenarios because of suddenly happened failures and 2) the existing DL models cannot be well implemented in different rotating components, which have different distributions and label space, such as from bearings to gears. To address these problems, a novel multiscale wavelet prototypical network (MWPN) is proposed in this study. It is designed to solve the few-shot fault diagnosis of the cross-component problem in rotating machines: the model is trained by one component with sufficient data and tested in another component with little data. First, a multiscale wavelet convolution module is designed to extract abundant features. Second, a metric meta-learner module is applied to measure the distance distribution between the labeled and unlabeled data. With the episode training strategy, the model is optimized and can adapt to similar tasks in a new machine and classify the unknown fault categories with few labeled samples. Experiments on three datasets are carried out to demonstrate the effectiveness of MWPN. Extensive experimental results show that MWPN outperforms many baseline methods on few-shot learning tasks in different working conditions and components.