In this paper,a cross-sensor generative self-supervised learning network is proposed for fault detection of multi-sensor.By modeling the sensor signals in multiple dimensions to achieve correlation information mining ...In this paper,a cross-sensor generative self-supervised learning network is proposed for fault detection of multi-sensor.By modeling the sensor signals in multiple dimensions to achieve correlation information mining between channels to deal with the pretext task,the shared features between multi-sensor data can be captured,and the gap between channel data features will be reduced.Meanwhile,in order to model fault features in the downstream task,the salience module is developed to optimize cross-sensor data features based on a small amount of labeled data to make warning feature information prominent for improving the separator accuracy.Finally,experimental results on the public datasets FEMTO-ST dataset and the private datasets SMT shock absorber dataset(SMT-SA dataset)show that the proposed method performs favorably against other STATE-of-the-art methods.展开更多
Different synthetic aperture radar(SAR)sensors vary significantly in resolution,polarization modes,and frequency bands,making it difficult to directly apply existing models to newly launched SAR satellites.These new s...Different synthetic aperture radar(SAR)sensors vary significantly in resolution,polarization modes,and frequency bands,making it difficult to directly apply existing models to newly launched SAR satellites.These new systems require large amounts of labeled data for model retraining,but collecting sufficient data in a short time is often infeasible.To address this contradiction,this paper proposes a data generation and transfer framework,integrating a stable diffusion model with attention distillation,that leverages historical SAR data to synthesize training data tailored to the unique characteristics of new SAR systems.Specifically,we fine-tune the low-rank adaptation(LoRA)modules within the multimodal diffusion transformer(MM-DiT)architecture to enable class-controllable SAR image generation guided by textual prompts.To ensure that the generated images reflect the statistical properties and imaging characteristics of the target SAR system,we further introduce an attention distillation mechanism that transfers sensor-specific features,such as spatial texture,speckle distribution,and structural patterns,from real target-domain data to the generative model.Extensive experiments on multi-class aircraft target datasets from two real spaceborne SAR systems demonstrate the effectiveness of the proposed approach in alleviating data scarcity and supporting cross-sensor remote sensing applications.展开更多
三维切削力传感器是智能数控车床的重要组成部分,通过切削力传感器可以间接分析出加工出现的各种问题,如工件加工质量和刀具磨损情况等。设计了一款低交叉干扰的全对中一体化车削力传感器,通过析因分析筛选设计变量,采取最佳空间填充技...三维切削力传感器是智能数控车床的重要组成部分,通过切削力传感器可以间接分析出加工出现的各种问题,如工件加工质量和刀具磨损情况等。设计了一款低交叉干扰的全对中一体化车削力传感器,通过析因分析筛选设计变量,采取最佳空间填充技术和有限元分析结合的方法生成实验设计模型,根据实验设计模型开发了灰狼算法优化的反向传播神经网络的高精度非线性代理模型,对比分析三种优化算法的Pareto前沿,选择TOP算法对代理模型进行多目标优化。优化后:传感器固有频率为1.561 k Hz,满足机床主轴转速在23415 r/min下使用,传感器的平均变形量提升了一倍,根据惠斯通电桥输出电压计算可得,传感器各方向灵敏度提升了10倍左右,Fc方向交叉干扰消除,整体交叉干扰最高为1.9%。展开更多
基金supported by the National Natural Science Foundation of China under Grant No.62173317the Key Research and Development Program of Anhui under Grant No.202104a05020064。
文摘In this paper,a cross-sensor generative self-supervised learning network is proposed for fault detection of multi-sensor.By modeling the sensor signals in multiple dimensions to achieve correlation information mining between channels to deal with the pretext task,the shared features between multi-sensor data can be captured,and the gap between channel data features will be reduced.Meanwhile,in order to model fault features in the downstream task,the salience module is developed to optimize cross-sensor data features based on a small amount of labeled data to make warning feature information prominent for improving the separator accuracy.Finally,experimental results on the public datasets FEMTO-ST dataset and the private datasets SMT shock absorber dataset(SMT-SA dataset)show that the proposed method performs favorably against other STATE-of-the-art methods.
基金supported in part by the National Natural Science Foundations of China(Nos.62201027,62271034)。
文摘Different synthetic aperture radar(SAR)sensors vary significantly in resolution,polarization modes,and frequency bands,making it difficult to directly apply existing models to newly launched SAR satellites.These new systems require large amounts of labeled data for model retraining,but collecting sufficient data in a short time is often infeasible.To address this contradiction,this paper proposes a data generation and transfer framework,integrating a stable diffusion model with attention distillation,that leverages historical SAR data to synthesize training data tailored to the unique characteristics of new SAR systems.Specifically,we fine-tune the low-rank adaptation(LoRA)modules within the multimodal diffusion transformer(MM-DiT)architecture to enable class-controllable SAR image generation guided by textual prompts.To ensure that the generated images reflect the statistical properties and imaging characteristics of the target SAR system,we further introduce an attention distillation mechanism that transfers sensor-specific features,such as spatial texture,speckle distribution,and structural patterns,from real target-domain data to the generative model.Extensive experiments on multi-class aircraft target datasets from two real spaceborne SAR systems demonstrate the effectiveness of the proposed approach in alleviating data scarcity and supporting cross-sensor remote sensing applications.
文摘三维切削力传感器是智能数控车床的重要组成部分,通过切削力传感器可以间接分析出加工出现的各种问题,如工件加工质量和刀具磨损情况等。设计了一款低交叉干扰的全对中一体化车削力传感器,通过析因分析筛选设计变量,采取最佳空间填充技术和有限元分析结合的方法生成实验设计模型,根据实验设计模型开发了灰狼算法优化的反向传播神经网络的高精度非线性代理模型,对比分析三种优化算法的Pareto前沿,选择TOP算法对代理模型进行多目标优化。优化后:传感器固有频率为1.561 k Hz,满足机床主轴转速在23415 r/min下使用,传感器的平均变形量提升了一倍,根据惠斯通电桥输出电压计算可得,传感器各方向灵敏度提升了10倍左右,Fc方向交叉干扰消除,整体交叉干扰最高为1.9%。
文摘传感器作为复杂装备监测系统的关键组成部分,若发生故障会引起误报警,极大影响复杂机械系统状态监测的可靠性。针对该难题,笔者从系统角度出发,提出一种基于去趋势互相关分析(detrended cross-correlation analysis,简称DCCA)和双尺度自编码器(dual auto encoder,简称DAE)的传感器故障检测方法,记作DCCA-DAE。首先,采用DCCA方法建立耦合网络,将数据从欧氏空间扩展到拓扑空间,实现对系统多源多态监测数据蕴含信息的全面表征;其次,构建基于DAE的异常检测方法,消除工况变化对传感器监测序列产生的影响,实现工况复杂变化下的系统传感器故障准确检测;最后,利用某电厂汽轮机组历史数据,验证所提方法的综合性能。结果表明,DCCA-DAE模型特征提取能力强,检测精度显著优于传统支持向量描述和自编码器等方法,在工业场景中传感器故障检测领域具有良好的应用前景。
