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  • Open Access

    ARTICLE

    Rolling Bearing Fault Diagnosis Method Based on FFT-VMD Multiscale Information Fusion and SE-TCN Model

    Chaozhi Cai, Yuqi Ren, Yingfang Xue*, Jianhua Ren

    Structural Durability & Health Monitoring, Vol.19, No.3, pp. 665-682, 2025, DOI:10.32604/sdhm.2025.059044 - 03 April 2025

    Abstract Rolling bearings are important parts of industrial equipment, and their fault diagnosis is crucial to maintaining these equipment’s regular operations. With the goal of improving the fault diagnosis accuracy of rolling bearings under complex working conditions and noise, this study proposes a multiscale information fusion method for fault diagnosis of rolling bearings based on fast Fourier transform (FFT) and variational mode decomposition (VMD), as well as the Senet (SE)-TCNnet (TCN) model. FFT is used to transform the original one-dimensional time domain vibration signal into a frequency domain signal, while VMD is used to decompose the… More >

  • Open Access

    ARTICLE

    A DFE2-SPCE Method for Multiscale Parametric Analysis of Heterogenous Piezoelectric Materials and Structures

    Qingxiang Pei1,2, Fan Li2,3, Ziheng Fei4, Haojie Lian2,3, Xiaohui Yuan1,2,*

    CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 79-96, 2025, DOI:10.32604/cmc.2025.061741 - 26 March 2025

    Abstract This paper employs the Direct Finite Element Squared (DFE2) method to develop Sparse Polynomial Chaos Expansions (SPCE) models for analyzing the electromechanical properties of multiscale piezoelectric structures. By incorporating variations in piezoelectric and elastic constants, the DFE2 method is utilized to simulate the statistical characteristics—such as expected values and standard deviations—of electromechanical properties, including Mises stress, maximum in-plane principal strain, electric potential gradient, and electric potential, under varying parameters. This approach achieves a balance between computational efficiency and accuracy. Different SPCE models are used to investigate the influence of piezoelectric and elastic constants on multiscale piezoelectric More >

  • Open Access

    ARTICLE

    MSCM-Net: Rail Surface Defect Detection Based on a Multi-Scale Cross-Modal Network

    Xin Wen*, Xiao Zheng, Yu He

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4371-4388, 2025, DOI:10.32604/cmc.2025.060661 - 06 March 2025

    Abstract Detecting surface defects on unused rails is crucial for evaluating rail quality and durability to ensure the safety of rail transportation. However, existing detection methods often struggle with challenges such as complex defect morphology, texture similarity, and fuzzy edges, leading to poor accuracy and missed detections. In order to resolve these problems, we propose MSCM-Net (Multi-Scale Cross-Modal Network), a multiscale cross-modal framework focused on detecting rail surface defects. MSCM-Net introduces an attention mechanism to dynamically weight the fusion of RGB and depth maps, effectively capturing and enhancing features at different scales for each modality. To… More >

  • Open Access

    ARTICLE

    Coupling Magneto-Electro-Elastic Multiscale Finite Element Method for Transient Responses of Heterogeneous MEE Structures

    Xiaolin Li1, Xinyue Li1, Liming Zhou2,*, Hangran Yang1, Xiaoqing Yuan1

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3821-3841, 2025, DOI:10.32604/cmc.2025.059937 - 06 March 2025

    Abstract Magneto-electro-elastic (MEE) materials are widely utilized across various fields due to their multi-field coupling effects. Consequently, investigating the coupling behavior of MEE composite materials is of significant importance. The traditional finite element method (FEM) remains one of the primary approaches for addressing such issues. However, the application of FEM typically necessitates the use of a fine finite element mesh to accurately capture the heterogeneous properties of the materials and meet the required computational precision, which inevitably leads to a reduction in computational efficiency. To enhance the computational accuracy and efficiency of the FEM for heterogeneous… More >

  • Open Access

    ARTICLE

    A Study of the 1 + 2 Partitioning Scheme of Fibrous Unitcell under Reduced-Order Homogenization Method with Analytical Influence Functions

    Shanqiao Huang1, Zifeng Yuan1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 2893-2924, 2025, DOI:10.32604/cmes.2025.059948 - 03 March 2025

    Abstract The multiscale computational method with asymptotic analysis and reduced-order homogenization (ROH) gives a practical numerical solution for engineering problems, especially composite materials. Under the ROH framework, a partition-based unitcell structure at the mesoscale is utilized to give a mechanical state at the macro-scale quadrature point with pre-evaluated influence functions. In the past, the “1-phase, 1-partition” rule was usually adopted in numerical analysis, where one constituent phase at the mesoscale formed one partition. The numerical cost then is significantly reduced by introducing an assumption that the mechanical responses are the same all the time at the… More >

  • Open Access

    ARTICLE

    SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

    Hongxing Wang1, Xilai Ju2, Hua Zhu1,*, Huafeng Li1,*

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 1417-1437, 2025, DOI:10.32604/cmc.2024.058785 - 03 January 2025

    Abstract Traditional data-driven fault diagnosis methods depend on expert experience to manually extract effective fault features of signals, which has certain limitations. Conversely, deep learning techniques have gained prominence as a central focus of research in the field of fault diagnosis by strong fault feature extraction ability and end-to-end fault diagnosis efficiency. Recently, utilizing the respective advantages of convolution neural network (CNN) and Transformer in local and global feature extraction, research on cooperating the two have demonstrated promise in the field of fault diagnosis. However, the cross-channel convolution mechanism in CNN and the self-attention calculations in… More > Graphic Abstract

    SEFormer: A Lightweight CNN-Transformer Based on Separable Multiscale Depthwise Convolution and Efficient Self-Attention for Rotating Machinery Fault Diagnosis

  • Open Access

    ARTICLE

    A Lightweight Multiscale Feature Fusion Network for Solar Cell Defect Detection

    Xiaoyun Chen1, Lanyao Zhang1, Xiaoling Chen1, Yigang Cen2, Linna Zhang1,*, Fugui Zhang1

    CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 521-542, 2025, DOI:10.32604/cmc.2024.058063 - 03 January 2025

    Abstract Solar cell defect detection is crucial for quality inspection in photovoltaic power generation modules. In the production process, defect samples occur infrequently and exhibit random shapes and sizes, which makes it challenging to collect defective samples. Additionally, the complex surface background of polysilicon cell wafers complicates the accurate identification and localization of defective regions. This paper proposes a novel Lightweight Multi-scale Feature Fusion network (LMFF) to address these challenges. The network comprises a feature extraction network, a multi-scale feature fusion module (MFF), and a segmentation network. Specifically, a feature extraction network is proposed to obtain… More >

  • Open Access

    PROCEEDINGS

    Deep-Potential Enabled Multiscale Simulation of Interfacial Thermal Transport in Boron Arsenide Heterostructures

    Jing Wu1, E Zhou1, An Huang1, Hongbin Zhang2, Ming Hu3, Guangzhao Qin1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.3, pp. 1-2, 2024, DOI:10.32604/icces.2024.012552

    Abstract High thermal conductivity substrate plays a significant role for efficient heat dissipation of electronic devices, and it is urgent to optimize the interfacial thermal resistance. As a novel material with ultra-high thermal conductivity second only to diamond, boron arsenide (BAs) shows promising applications in electronics cooling [1,2]. By adopting multi-scale simulation method driven by machine learning potential, we systematically study the thermal transport properties of boron arsenide, and further investigate the interfacial thermal transport in the GaN-BAs heterostructures. Ultrahigh interfacial thermal conductance of 260 MW m-2K-1 is achieved, which agrees well with experimental measurements, and the More >

  • Open Access

    PROCEEDINGS

    Inductive and Deductive Scale-Bridging In Hierarchical Multiscale Models for Dislocation Pattern Formation in Metal Fatigue

    Yoshitaka Umeno1,*, Atsushi Kubo2, Emi Kawai1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-2, 2024, DOI:10.32604/icces.2024.012708

    Abstract Fatigue fracture accounts for a substantial fraction of failure cases in industrial products, especially in metal materials. While the mechanism of fatigue crack propagation can be understood in the mechanical point of view considering the effect of microstructures and crystal orientations on crack growth, there is still much room for investigations of the mechanism of fatigue crack formation under cyclic loading. It is widely understood that the fatigue crack formation in macroscopic metal materials originates in the persistent slip band (PSB) formed as a result of self-organization of dislocation structures [1]. Nevertheless, the PSB formation… More >

  • Open Access

    PROCEEDINGS

    Multiscale Optimization of Non-Linear Structures

    Ryan Murphy1,*, Dilaksan Thillaithevan1, Matthew Santer1, Rob Hewson1

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.32, No.1, pp. 1-1, 2024, DOI:10.32604/icces.2024.011402

    Abstract In this work we describe the multiscale optimization of non-linear structures. This work moves beyond classical multiscale optimization for linear problems to account for large deformations occurring across the scales of the problem. A multiscale approach is adopted based on the homogenization theory which is used to characterize a parameterized representative volume element (RVE). This RVE characterization is undertaken for both changes in the geometry and the strain applied to the RVE. This latter is a key difference between multiscale approaches for non-linear problems and those for linear problems. This is because the characteristics of… More >

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