Multiscale capsule networks with attention mechanisms based on domain-invariant properties for cross-domain lifetime prediction

Abstract

Remaining useful life prediction (RUL) under variable operating conditions has been a hot research topic in engineering. Still, current unsupervised domain adaptive (UDA) methods all use a single metric (MK-MMD or adversarial mechanism), which somewhat improves the cross-domain RUL prediction performance. However, using MK-MMD or the adversarial mechanism alone to measure the inter-domain differences has a single perspective. At the same time, MK-MMD focuses on measuring the differences in the feature distribution layer and fails to capture the differences in the nonlinear relationship between the source and target domains. In addition, learning high-quality degraded features is also a vital issue in RUL prediction. Based on the above points, this paper proposes an attention mechanism multiscale capsule network cross-domain lifetime prediction method (DI-MCNAM) based on domain invariant properties. First, the attention mechanism multiscale capsule network (MCNAM) extracts the deep degradation features of the weighted data. Second, two modules, multilevel domain adaptation and domain classifier, are integrated to reduce inter-domain differences from different perspectives. The maximum information coefficient (MIC) is introduced in the multilevel domain adaptation module. MIC can learn the nonlinear relationship between source and target domain data over time separately, combined with MK-MMD, to reduce the inter-domain feature differences from different levels. Domain classifiers discriminate the confusing domains and reduce inter-domain differences by minimizing the classification power. The performance of the proposed model is validated using publicly available datasets and compared with existing popular methods, and the final results show that the proposed method has high prediction accuracy.