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- 저자 : Zhou Menglin (검색결과 2 건)
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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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Liang Li,Zhang Fengmei,Feng Naibo,Kuang Biao,Fan Mengtian,Chen Cheng,Pan Yiming,Zhou Pengfei,Geng Nana,Li Xingyue,Xian Menglin,Deng Lin,Li Xiaoli,Kuang Liang,Luo Fengtao,Tan Qiaoyan,Xie Yangli,Guo Fen 생화학분자생물학회 2023 Experimental and molecular medicine Vol.55 No.-
Osteoarthritis (OA) is a full-joint, multifactorial, degenerative and inflammatory disease that seriously affects the quality of life of patients due to its disabling and pain-causing properties. ER stress has been reported to be closely related to the progression of OA. The inositol-requiring enzyme 1α/X-box-binding protein-1 spliced (IRE1α/XBP1s) pathway, which is highly expressed in the chondrocytes of OA patients, promotes the degradation and refolding of abnormal proteins during ER stress and maintains the stability of the ER environment of chondrocytes, but its function and the underlying mechanisms of how it contributes to the progression of OA remain unclear. This study investigates the role of IRE1α/ERN1 in OA. Specific deficiency of ERN1 in chondrocytes spontaneously resulted in OA-like cartilage destruction and accelerated OA progression in a surgically induced arthritis model. Local delivery of AdERN1 relieved degradation of the cartilage matrix and prevented OA development in an ACLT-mediated model. Mechanistically, progranulin (PGRN), an intracellular chaperone, binds to IRE1α, promoting its phosphorylation and splicing of XBP1u to generate XBP1s. XBP1s protects articular cartilage through TNF-α/ERK1/2 signaling and further maintains collagen homeostasis by regulating type II collagen expression. The chondroprotective effect of IRE1α/ERN1 is dependent on PGRN and XBP1s splicing. ERN1 deficiency accelerated cartilage degeneration in OA by reducing PGRN expression and XBP1s splicing, subsequently decreasing collagen II expression and triggering collagen structural abnormalities and an imbalance in collagen homeostasis. This study provides new insights into OA pathogenesis and the UPR and suggests that IRE1α/ERN1 may serve as a potential target for the treatment of joint degenerative diseases, including OA.
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