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Haichao Feng,Yaohua Guo,Xiaozhuo Xu,Caixia Gao,Liwang Ai 한국자기학회 2023 Journal of Magnetics Vol.28 No.4
In order to meet the demand of high-power density, high-precision positioning and low cost direct drive system in ropeless lifting, precision machining and other fields, the modular secondary structure with permanent magnet synchronous magnetization is adopted in this paper, and the secondary permanent magnet flux-switching linear machine (SPMFSLM) is proposed. Compared with the traditional primary permanent magnet fluxswitching linear machine (PPMFSLM), it reduces the thrust ripple and strengthens the primary structure. According to the topology and operation principle of the machine, starting from the air-gap magnetic field modulation principle and the convex tooth effect, the contribution of the air-gap flux density harmonics of the permanent magnet magnetic field and the armature magnetic field for the two machines to the average thrust is compared and analyzed, reflecting the similarities and differences between the two FSLMs in the thrust generation mechanism. In addition, the no-load characteristics for back-EMF and the thrust characteristics for two machines under equal load are calculated and compared. Finally, the multi-objective optimization of the key structural parameters of the machine is carried out by combining the response surface method (RSM) with the NSGAII algorithm. Then, the 3D simulation of the machine was carried out and cross-validated with the 2D results. Finally, a double-sided SPMFSLM (DS-SPMFSLM) is proposed from the practical point of view. The performance of DS-SPMFSLM is significantly superior to that of single-sided SPMFSLM, which verifies the advantages of the secondary permanent magnet structure scheme.
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Bearing Fault Diagnosis of Single-Channel Data by a 3D DCN with Bilinear LBP and Modified KPCA
Zhao Yunji,Zhou Menglin,Wang Li,Xu Xiaozhuo,Zhang Nannan 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.5
The vibration signal has the characteristics of nonlinear and non-stationary, and the distribution of fault feature information contained in it is not concentrated. In addition, the nonlinear coupling of fault-adjacent features in space is strong, resulting in poor spatial separability of fault information. At the same time, the fault diagnosis algorithm based on a convolutional neural network, cannot fully obtain the spatial distribution information of fault data due to its fixed geometric structure of convolution kernel. In order to solve the above problems, a novel fault diagnosis method for single-channel bearing fault data is proposed. First, the improved bilinear local binary pattern algorithm is used to extract time series constraint information between different points of the original fault data. Then, considering the strong nonlinear coupling of fault data adjacent features in space, this paper proposes the modified kernel principal component analysis. It obtains information on fault data in high-dimensional space by calculating the kernel space mapping matrix of different fault categories, kernelizing the sample matrix, and mapping the kernel space mapping matrix. Finally, based on this information, a 3D deformable convolution network (DCN) is introduced to obtain the spatial distribution information of fault data. DCN can adaptively adjust the shape of its own convolution kernel according to the input, which can obtain more comprehensive information and further improve the spatial separability of fault data. Experiments on CWRU and XJTU-SY both achieved 100% diagnostic accuracy, which shows the superiority of the proposed method.
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