TScatNet: An Interpretable Cross-Domain Intelligent Diagnosis Model With Antinoise and Few-Shot Learning Capability

Abstract

In a real industrial scenario, domain shift frequently occurred due to working loads variation, operation speeds variation, and environmental noise interference, severely degrading intelligent fault diagnosis models' performance. Currently, domain adaptation-based models eliminate domain shift by calibrating unlabeled target-domain data using labeled source-domain data. Nevertheless, these models may fail when encountering unseen working conditions, lacking unlabeled target-domain data for learning domain-invariant features. Besides, the existing deep domain adaptation-based models lack a few-shot learning capability and interpretability. This article develops a cross-domain diagnosis model named time-scattering convolutional network (TScatNet) to remedy these gaps. TScatNet extracts domain-invariant features using Morlet wavelet as the predefined convolutional kernel, modulus as nonlinearity, and scaling averaging as pooling layer. This predefined architecture eliminates domain shift without any domain adaptation, endows TScatNet few-shot learning capability, simplifies the hyperparameter tuning process, and brings interpretability. Both the CWRU and DDS data sets are used to verify the proposed model, which shows that TScatNet could stably realize 100% accuracy on transfer tasks across working loads and 100% accuracy across operation speeds. Moreover, even though the SNR value descends to -4, TScatNet achieved 96% accuracy on ten categories tasks and 99.8% accuracy on four categories tasks. Besides, TScatNet achieved nearly 100% accuracy both under training samples' sparsity and sparsity of working conditions.