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A Railway Fastener Inspection Method Based on Abnormal Sample Generation

Computer Modeling in Engineering & Sciences 2024年第4期139卷 565-592页

作者： Shubin Zheng Yue Wang Liming Li Xieqi Chen lele peng Zhanhao Shang Higher Vocational and Technical College Shanghai University of Engineering ScienceShanghai200437China School of Urban Railway Transportation Shanghai University of Engineering ScienceShanghai201620China Shanghai Engineering Research Centre of Vibration and Noise Control Technologies for Rail Transit Shanghai University of Engineering ScienceShanghai201620China

Regular fastener detection is necessary to ensure the safety of ***,the number of abnormal fasteners is significantly lower than the number of normal fasteners in real *** supervised inspectionmethods have insufficient detection ability in cases of imbalanced *** solve this problem,we propose an approach based on deep convolutional neural networks(DCNNs),which consists of three stages:fastener localization,abnormal fastener sample generation based on saliency detection,and fastener state ***,a lightweight YOLOv5s is designed to achieve fast and precise localization of fastener ***,the foreground clip region of a fastener image is extracted by the designed fastener saliency detection network(F-SDNet),combined with data augmentation to generate a large number of abnormal fastener samples and balance the number of abnormal and normal ***,a fastener inspection model called Fastener ResNet-8 is constructed by being trained with the augmented fastener *** show the effectiveness of our proposed method in solving the problem of sample imbalance in fastener *** and quantitative comparisons show that the proposed F-SDNet outperforms other state-of-the-art methods in clip region extraction,reaching MAE and max F-measure of 0.0215 and 0.9635,*** addition,the FPS of the fastener state inspection model reached 86.2,and the average accuracy reached 98.7%on 614 augmented fastener test sets and 99.9%on 7505 real fastener datasets.

关键词： Railway fastener sample generation inspection model deep learning

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Design principle of single-atom catalysts for sulfur reduction reaction—interplay between coordination patterns and transition metals

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Science China Materials 2024年第10期67卷 3215-3224页

作者： Wentao Zhang Gaoshang Zhang Zhaotian Xie Xinming Zhang Jiabin Ma Ziyao Gao Kuang Yu lele peng Tsinghua Shenzhen International Graduate School Tsinghua UniversityShenzhen 518055China School of Materials Science and Engineering Tsinghua UniversityBeijing 100084China

The polysulfide shuttling effect is the primary bottleneck restricting the industrial application of Li-S batteries,and the electrocatalytic sulfur reduction reaction(SRR)has emerged as an effective ***-based singleatom catalysts(SACs),which promotes SRR,show great potential in inhibiting the shuttling effect of ***,the optimization and rational design of such catalysts requires a deep understanding to the fundamental SRR mechanism and remains highly *** this work,we construct a comprehensive database of carbon-based SACs,covering different coordination patterns,heteroatoms,and transition *** SRR activities are determined using density functional theory calculations,revealing a synergistic effect between the p orbital of the heteroatom and the d orbital of the transition *** interplay underscores the critical importance of the coordination environment for SRR under the ortho-P_(2)C_(2)*** of the transition metal type,the ortho-P_(2)C_(2)coordination pattern significantly enhances the SRR performance of SACs,surpassing the widely reported N_(3)C_(1)and N_(4)coordinated graphene-based ***,heteroatoms with ortho-P_(2)C_(2)may exhibit SRR *** a word,by using this comprehensive dataset and data-driven framework,we propose a promising novel class of coordination structure(ortho-P_(2)C_(2)structure)and neglected design principle.

关键词： single-atom catalysts ortho-P doped structure abinitio calculations machine learning sulfur reduction reaction

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基于振动信号图像特征的降噪残差网络轴承故障诊断

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噪声与振动控制 2024年第3期44卷 109-116,169页

作者：陶俊鹏张玮东钟倩文彭乐乐郑树彬陈谢祺上海工程技术大学城市轨道交通学院上海201620 上海地铁维护保障有限公司车辆分公司上海200031

针对将一维原始轴承振动信号作为既有轴承诊断模型的输入所致训练效率、抗噪性欠佳的问题,提出一种基于振动信号图像特征的自适应降噪残差网络轴承故障诊断方法。首先将一维轴承振动信号进行截断、重叠采样后重构成信号矩阵,最后将其编... 详细信息

针对将一维原始轴承振动信号作为既有轴承诊断模型的输入所致训练效率、抗噪性欠佳的问题,提出一种基于振动信号图像特征的自适应降噪残差网络轴承故障诊断方法。首先将一维轴承振动信号进行截断、重叠采样后重构成信号矩阵,最后将其编码为图像得到振动信号图像;再对图像进行直方图处理,计算得到其灰度分布特征矩阵,并将振动信号图像和对应的特征矩阵作为算法模型的输入;同时,在提出的网络模型中在残差卷积映射的过程中插入基于通道注意力机制的降噪路径,通过自适应地获得阈值进行降噪,提高网络对含噪声样本的故障特征提取能力。最后通过对比实验证明:网络模型在加入灰度分布特征后有更好的性能表现,提出的自适应降噪残差网络模型在将含有噪声的振动信号作为输入的情况下仍具有较高的故障识别精度。

关键词：故障诊断图像特征通道注意力机制降噪残差神经网络

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基于CEEMDAN-FastICA-MCNN的多传感信息融合轴承故障诊断

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噪声与振动控制 2024年第4期44卷 145-152页

作者：张鑫钟倩文余佑民彭乐乐郑树彬陈谢祺上海工程技术大学城市轨道交通学院上海201620 上海地铁维护保障有限公司车辆分公司上海200031

针对轴承振动信号易受噪声干扰、变工况及单一传感器提取特征信息不完备的问题,提出基于自适应噪声完备集成经验模态分解(Complete Ensemble Empirical Mode Decomposition of Adaptive Noise,CEEMDAN)、快速独立分量(Fast Independent ... 详细信息

针对轴承振动信号易受噪声干扰、变工况及单一传感器提取特征信息不完备的问题,提出基于自适应噪声完备集成经验模态分解(Complete Ensemble Empirical Mode Decomposition of Adaptive Noise,CEEMDAN)、快速独立分量(Fast Independent Components Analysis,FastICA)降噪和多输入卷积神经网络(Multiple-input Convolutional Neural Networks,MCNN)的多传感信息融合轴承故障诊断方法。首先分别对多传感采集的振动信号划分数据集,并输入CEEMDAN得到本征模态函数(Inherent Nodal Function,IMF);随后,选择峭度大于3的IMF构造观测信号,其余IMF构造虚拟噪声信号,作为两个输入源输入FastICA,分离出特征向量;最后,设计MCNN识别故障类型。在CWRU和XJTU-SY数据集上的正确率分别为99.94%和99.64%。在信噪比为-8 dB的抗噪性能测试中,正确率分别为96.95%和98.29%;在信噪比为0 dB的抗噪性能测试中,正确率分别为99.00%和99.23%。对比实验结果表明此方法能够提取更为全面的故障特征信息,获得更高的准确率。

关键词：故障诊断 CEEMDAN FastICA MCNN

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A Detection Method of Bolts on Axlebox Cover Based on Cascade Deep Convolutional Neural Network

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Computer Modeling in Engineering & Sciences 2023年第3期134卷 1671-1706页

作者： Ji Wang Liming Li Shubin Zheng Shuguang Zhao Xiaodong Chai lele peng Weiwei Qi Qianqian Tong School of Urban Railway Transportation Shanghai University of Engineering ScienceShanghai201620China School of Information Science and Technology Donghua UniversityShanghai201620China Shanghai Engineering Research Centre of Vibration and Noise Control Technologies for Rail Transit Shanghai University of Engineering ScienceShanghai201620China

This paper proposes a cascade deep convolutional neural network to address the loosening detection problem of bolts on axlebox ***,an SSD network based on ResNet50 and CBAM module by improving bolt image features is proposed for locating bolts on axlebox *** then,theA2-PFN is proposed according to the slender features of the marker lines for extracting more accurate marker lines regions of the ***,a rectangular approximationmethod is proposed to regularize themarker line regions asaway tocalculate the angle of themarker line and plot all the angle values into an angle table,according to which the criteria of the angle table can determine whether the bolt with the marker line is in danger of ***,our improved algorithm is compared with the pre-improved algorithmin the object localization *** results show that our proposed method has a significant improvement in both detection accuracy and detection speed,where ourmAP(IoU=0.75)reaches 0.77 and fps reaches *** in the saliency detection stage,after qualitative comparison and quantitative comparison,our method significantly outperforms other state-of-the-art methods,where our MAE reaches 0.092,F-measure reaches 0.948 and AUC reaches ***,according to the angle table,out of 676 bolt samples,a total of 60 bolts are loose,69 bolts are at risk of loosening,and 547 bolts are tightened.

关键词： Loosening detection cascade deep convolutional neural network object localization saliency detection

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Prelithiation strategies for silicon-based anode in high energy density lithium-ion battery

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Green Energy & Environment 2023年第5期8卷 1325-1340页

作者： Tianqi Jia Geng Zhong Yao Lv Nanrui Li Yanru Liu Xiaoliang Yu Jinshuo Zou Zhen Chen lele peng Feiyu Kang Yidan Cao Shenzhen Geim Graphene Center Institute of Materials ResearchShenzhen International Graduate SchoolTsinghua UniversityShenzhen518055China Tsinghua-Berkeley Shenzhen Institute Tsinghua UniversityShenzhen518055China College of Sciences and Institute for Sustainable Energy Shanghai UniversityShanhai200000China Department of Mechanical Engineering The Hong Kong Polytechnic UniversityHong Kong999077China School of Chemical Engineering&Advanced Materials The University of AdelaideAdelaideSA 5005Australia

Green energy storage devices play vital roles in reducing fossil fuel emissions and achieving carbon neutrality by *** markets for portable electronics and electric vehicles create tremendous demand for advanced lithium-ion batteries(LIBs)with high power and energy density,and novel electrode material with high capacity and energy density is one of the keys to next-generation ***-based materials,with high specific capacity,abundant natural resources,high-level safety and environmental friendliness,are quite promising alternative anode ***,significant volume expansion and redundant side reactions with electrolytes lead to active lithium loss and decreased coulombic efficiency(CE)of silicon-based material,which hinders the commercial application of silicon-based ***,preembedding extra lithium ions in the electrodes,is a promising approach to replenish the lithium loss during *** progress on prelithiation strategies for silicon-based anode,including electrochemical method,chemical method,direct contact method,and active material method,and their practical potentials are reviewed and prospected *** development of advanced Si-based material and prelithiation technologies is expected to provide promising approaches for the large-scale application of silicon-based materials.

关键词： Si-based materials Prelithiation Coulombic efficiency Lithium loss Lithium-ion battery

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强噪声背景下地铁牵引电机轴承故障识别方法研究

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计算机与数字工程 2024年第7期52卷 2239-2243页

作者：王锦畅陈威彭乐乐郑树彬钟倩文上海工程技术大学城市轨道交通学院上海201620 上海航天设备制造总厂有限公司上海200245

为了实现地铁车辆牵引电机轴承故障识别,论文针对电机轴承故障冲击被强背景噪声淹没特征提取困难的问题,利用多点最优调整的最小熵解卷积增强故障冲击成分,采用粒子群优化算法自适应地确定滤波器阶数和故障周期,获取高信噪比的故障特征... 详细信息

为了实现地铁车辆牵引电机轴承故障识别,论文针对电机轴承故障冲击被强背景噪声淹没特征提取困难的问题,利用多点最优调整的最小熵解卷积增强故障冲击成分,采用粒子群优化算法自适应地确定滤波器阶数和故障周期,获取高信噪比的故障特征信号,最后对故障特征信号进行包络谱分析实现故障识别。现场采集数据验证了该方法的有效性。

关键词：牵引电机轴承故障诊断多点最优调整的最小熵解卷积粒子群优化

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The promises, challenges and pathways to room-temperature sodium-sulfur batteries

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National Science Review 2022年第3期9卷 144-168页

作者： Lei Wang Tao Wang lele peng Yiliu Wang Meng Zhang Jian Zhou Maoxin Chen Jinhui Cao Huilong Fei Xidong Duan Jian Zhu Xiangfeng Duan State Key Laboratory for Chemo Biosensing and Chemometricsand College of Chemistry and Chemical EngineeringHunan Key Laboratory of Two-Dimensional Materials Hunan University Department of Chemistry and Biochemistry University of CaliforniaLos Angeles

Room-temperature sodium-sulfur batteries(RT-Na-S batteries) are attractive for large-scale energy storage applications owing to their high storage capacity as well as the rich abundance and low cost of the ***, their practical application is hampered by severe challenges, such as low conductivity of sulfur and its reduced products, volume expansion, polysulfide shuttling effect and Na dendrite formation,which can lead to rapid capacity fading. The review discusses the Na-S-energy-storage chemistry,highlighting its promise, key challenges and potential strategies for large-scale energy storage ***, we review the electrochemical principles and the current technical challenges of RT-Na-S batteries, and discuss the strategies to address these obstacles. In particular, we give a comprehensive review of recent progresses in cathodes, anodes, electrolytes, separators and cell configurations, and provide a forward-looking perspective on strategies toward robust high-energy-density RT-Na-S batteries.

关键词： room-temperature sodium-sulfur batteries sulfur cathodes polysulfide shuttling effects Na dendrite formation electrocatalysis

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基于CNN-LSTM的地铁空调控制器剩余使用寿命预测分析

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集成电路应用 2024年第6期41卷 402-403页

作者：张浩伟彭乐乐郑树彬刘波张超超高昂上海工程技术大学城市轨道交通学院上海201620

阐述综合考虑地铁空调控制器的特点,构建基于功能层及经济层的空调控制器样本数据集,通过建立CNN-LSTM模型对空调控制器的剩余使用寿命进行预测。实验结果表明,该预测模型准确度较高。

关键词：空调控制器剩余使用寿命预测样本数据集 CNN-LSTM

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Single-wall carbon nanotube fiber non-woven fabrics with a high electrothermal heating response

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Nano Research 2024年第6期17卷 5621-5628页

作者： Xinyu Jiao lele Xu Xinyang Sun Chao Shi peng-Xiang Hou Chang Liu Hui-Ming Cheng Shenyang National Laboratory for Materials Science Institute of Metal ResearchChinese Academy of SciencesShenyang 110016China School of Materials Science and Engineering University of Science and Technology of ChinaHefei 230026China Faculty of Materials Science and Engineering/Institute of Technology for Carbon Neutrality Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518055China

Carbon nanotube(CNT)fibers have great promise for constructing multifunctional fabrics with high electrical conductivity,good electro-heating ability,excellent flexibility,and a low ***,the inter-fiber contacts in the fabric greatly reduce these advantages and limit their ***,a simple pressure-fusing method to fabricate single-wall CNT(SWCNT)fiber non-woven fabrics(NWFs)that are composed of interconnected SWCNT fibers with fused joints is reported,which have good flexibility,a low density of 0.46 g/cm^(3),a high electrical conductivity of 3.7×10^(5)S/m,and a record high specific electrical conductivity of 803(S·m^(2))/*** also showed excellent electrical heating ability,so that a temperature of~160℃was rapidly reached at a low voltage of 2 *** with their low density,the SWCNT fiber NWFs are promising for use as a heating unit for low temperature battery protection and de-icing applications.

关键词： carbon nanotube fiber nonwoven fabrics fusing electrothermal heating

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