A Concentrated-Flux-Type PM Machine With Irregular Magnets and Iron Poles

Xiang, Pengjie; Yan, Liang; Guo, Yiming; He, Xinghua; Gerada, Chris; Chen, I‐Ming

doi:10.1109/tmech.2023.3293505

Cited by 26 publications

(2 citation statements)

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“…the contrary, the modifications to M S and M T maximize the L advD in equation (2), which are designed to confuse M S (X S ) with M T (X T ). As mentioned in section 2.1, M S and C S are pre-trained with X S and Y S , and then C S is directly transferred to C T .…”

Section: õNmentioning

confidence: 99%

“…Hydraulic transmission systems are widely applied in modern industrial settings because of their high power density, high precision, and rapid dynamic response [1,2]. As the important power elements in hydraulic transmission systems, axial piston pumps are responsible for converting mechanical energy coming from a prime mover into hydraulic energy [3].…”

Section: Introductionmentioning

confidence: 99%

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Fault severity recognition in axial piston pumps using attention-based adversarial discriminative domain adaptation neural network

Shao,

Chao,

Xia

et al. 2024

Phys. Scr.

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Axial piston pumps are the ‘hearts’ of hydraulic systems whose fault recognition is necessary for the safety and reliability of hydraulic equipment. These pumps operate under different operating conditions and the fault recognition model trained at one operating point cannot be applicable at another operating point due to the problem of domain shifts. This paper proposes a transfer learning method for the fault severity recognition of axial piston pumps based on adversarial discriminative domain adaptation fused with a convolutional channel attention module. First, a convolutional neural network is pre-trained with labeled vibration data from the source domain, and a convolutional channel attention module is added to assign weights to different convolution kernels. Second, the trained source model is transferred to the target domain, and its parameters are updated by an adversarial training process between the labeled source data and the unlabeled target data. Finally, vibration data are collected from an axial piston pump at different fault levels under various operating conditions to validate the proposed method. Experimental results indicate that the proposed method achieves an average recognition accuracy of 98.3% and outperforms some other transfer learning methods by a large margin.

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Section: õNmentioning

confidence: 99%