Xuyao Lu, Yongjie Huang, Ruiqi Liu, Xiaofei Huang, Chuanzhu Liu
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引用次数: 0
Abstract
Controller area network (CAN) buses are widely used as low-cost, highly flexible field buses in various scenarios, such as in vehicle networks for automobiles and communication networks for industrial sites. They typically operate in harsh environments, and faults inevitably occur. CAN bus faults cannot be efficiently diagnosed via traditional manual detection. Herein, we propose a lightweight MDSCA-Net for CAN bus fault diagnosis. Deep separable convolution (DSConv) is used in the model instead of ordinary convolution to reduce the number of parameters and floating-point operations. Additionally, the noise immunity of the model is improved by designing a multiscale denoising module (MDM). A multiscale deep separable convolutional fusion SE at tention (MDSCSA) module is designed to capture the channel dimension details of the features. Furthermore, a spatial attention module (SAM) is utilized to capture the spatial dimension details of the features. Finally, a residual (Res) module stabilizes the model performance. Experimental results on the CAND dataset indicated that the proposed method achieved a diagnostic accuracy of 99% in a noise-free environment, and compared with other fault diagnosis methods, it had better noise immunity and robustness in a noisy environment, which is of considerable practical significance for ensuring the stable operation of CAN buses.
控制器局域网(CAN)总线作为低成本、高灵活性的现场总线被广泛应用于各种场合,如汽车的车载网络和工业现场的通信网络。它们通常在恶劣的环境中运行,不可避免地会出现故障。传统的人工检测无法有效诊断 CAN 总线故障。在此,我们提出了一种用于 CAN 总线故障诊断的轻量级 MDSCA 网络。该模型采用深度可分离卷积(DSConv)代替普通卷积,以减少参数和浮点运算的数量。此外,还通过设计多尺度去噪模块(MDM)提高了模型的抗噪能力。设计了一个多尺度深度可分离卷积融合 SE at tention(MDSCSA)模块,以捕捉通道维度的特征细节。此外,空间关注模块(SAM)用于捕捉特征的空间维度细节。最后,残差(Res)模块可稳定模型性能。在 CAND 数据集上的实验结果表明,所提出的方法在无噪声环境下的诊断准确率达到了 99%,与其他故障诊断方法相比,在噪声环境下具有更好的抗噪性和鲁棒性,这对于确保 CAN 总线的稳定运行具有重要的现实意义。
期刊介绍:
ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications.
The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.