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Considering the difficulties of IFD using only current-related information from the motor current signal (MCS), this paper proposes a three-dimensional hybrid-fusion neural network (3D-HFN) that can automatically perform both data- and feature-level fusion of multi-phase current signals for MCS-based IFD of the rolling bearing.

This paper presents a deep learning-based multi-fidelity optimization framework to improve the uniformity of the scattered acoustic field distribution.

This paper explores a novel method based on metric-based meta-learning for the classification of welding defects with cross-domain few-shot (CDFS) problems.

This research explores a data-driven analytical framework for multisensory system analysis and design in PHM. The proposed framework provides the optimal subset of reliable sensors to make trade-offs between accuracy demands and system constraints.

The cross-attention fusion neural network (CAFNet) was proposed to interactively capture photoelectric and acoustic information for effective quality classification without prior time–frequency analysis and feature learning.

An improved SAE based on convolutional shortcuts and domain fusion strategy (ISAE-CSDF) is proposed for fault diagnosis of rolling bearing

This paper presents a multi-fidelity point-cloud neural network method (MF-PointNN) for surrogate modeling of melt pool based on FE simulation data.

In this work, a deep learning method is developed for in situ part quality inspection. The layer-wise visual images are used as the inputs without manual feature extraction and a deep transfer learning (DTL) model combining deep convolutional neural network and transfer learning is creatively applied.

In this project, a novel intelligent diagnosis method based on Multi-Sensor Fusion (MSF) and Convolutional Neural Network (CNN) is explored.

Therefore, a novel method based on recurrent neural networks (RNNs) is proposed to identify fault types in rotating machinery in this paper.