2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)最新文献

Thickness Measurement of Laminated Structure Based on Harmonic Eddy Current Technology 基于谐波涡流技术的层合结构厚度测量

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058433

Han Shi, Quanxin Zhou, Kai Chen, Guiyun Leng, Z. Zhong

{"title":"Thickness Measurement of Laminated Structure Based on Harmonic Eddy Current Technology","authors":"Han Shi, Quanxin Zhou, Kai Chen, Guiyun Leng, Z. Zhong","doi":"10.1109/ICSMD57530.2022.10058433","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058433","url":null,"abstract":"Laminated structural materials are widely used in various fields, and the simultaneous measurement of the thickness of multilayer materials with different physical properties is a difficult problem. It has significant academic value to measure the thickness of multi-layer materials. This article aims at the synthetic fuel rod shell structure, and establishes a laminated conductive structure impedance model with non-conductive layer. The viability of measuring the thickness of non-conductive layer and conductive layer is verified by Comsol finite element simulation. The traditional eddy current method can measure the thickness of non-conductive layer. The analytical model of laminated structure can fit the thickness of conductive layer by measuring impedance. In the end, the impedance analytical model effectively reduces the influence of skin depth and lift off effect, the thicknesses of two layers of different materials are obtained, which provides a theoretical basis and a quick method for the thickness measurement of complex laminated structures.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115160119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Remaining Useful Life Prediction for Complex Electro-Mechanical System Based on Conditional Generative Adversarial Networks 基于条件生成对抗网络的复杂机电系统剩余使用寿命预测

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058338

YiCong Duan, Yu-Fang Peng, Jianbao Zhou, Muyao Xue

{"title":"Remaining Useful Life Prediction for Complex Electro-Mechanical System Based on Conditional Generative Adversarial Networks","authors":"YiCong Duan, Yu-Fang Peng, Jianbao Zhou, Muyao Xue","doi":"10.1109/ICSMD57530.2022.10058338","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058338","url":null,"abstract":"Remaining Useful Life (RUL) prediction is of significance to provide valuable information for implementing condition-based maintenance and repair. Except for the difficulty on formulating the physical model of the complex electro-mechanical system, another challenge is how to utilize the sparse samples to achieve accurate prediction results. To address this issue, this paper proposes a novel RUL prediction method based on the sample augmentation by the improved Conditional Generative Adversarial Networks (CGAN). The aircraft Auxiliary Power Unit (APU) is taken as a typical complex electro-mechanical object. Two-dimensional condition monitoring samples of the aircraft APU contain the potential degradation information, which bring difficulty for formulating an accurate and stable RUL prediction model. First, its two-dimension condition monitoring samples are augmented by the improved CGAN. Then, the augmented samples and the original samples are both utilized as the input of the RUL prediction method. Through comparison experiments on a practical sample set, the effectiveness of the proposed method is evaluated by different RUL prediction methods and combinations of samples.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123084684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Accelerated algorithm for BTT identification parameter with GMC sparse regularization 基于GMC稀疏正则化的BTT参数识别加速算法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058283

Yuda Zhu, Baijie Qiao, Yanan Wang, Bo Pan, Lin Chen, Xuefeng Chen

{"title":"Accelerated algorithm for BTT identification parameter with GMC sparse regularization","authors":"Yuda Zhu, Baijie Qiao, Yanan Wang, Bo Pan, Lin Chen, Xuefeng Chen","doi":"10.1109/ICSMD57530.2022.10058283","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058283","url":null,"abstract":"Accurate identification of vibration parameters from blade tip timing (BTT) undersampled signals is essential for rotating blade vibration monitoring. However, the traditional parameter identification method of BTT signal depends on the prior. The existing sparse regularization method underestimates the reconstructed signal amplitude and has low computational efficiency. This paper resorts to an accelerated algorithm for BTT identification parameters based on generalized minimax-concave (GMC) sparse regularization to accurately and quickly identify amplitude and frequency parameters from undersampled signals. For amplitude underestimation, the non-convex GMC penalty is introduced so that the sparsity of the estimation is improved, and the convexity of the cost function is preserved. Moreover, Nesterov's accelerated iterative computation strategy is resorted to rapidly improving the convergence performance of obtaining the global optimum. The simulation results show that by reconstructing the BTT signal, the presented parameter identification algorithm based on accelerated generalized minimax-concave (AGMC) improves the computational rate with the inherited merits of accuracy.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117227197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Overview of the Development of Origami-inspired Robot Arms 折纸机器人手臂的发展概述

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058280

Yixue Liu, Laihao Yang, Yu Sun

引用次数: 0

Indoor Human Activity Recognition using Millimeter-Wave Radio Signals 利用毫米波无线电信号识别室内人体活动

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058412

X. Shen, Yuyong Xiong, Songxu Li, Zhike Peng

引用次数: 0

Improved Nonlinear Ultrasound Sensing based on 3D Printing Phonon Crystals 基于3D打印声子晶体的改进非线性超声传感

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058317

Wenkang Li, Hailin Cao, Liuyang Zhang, Xuefeng Chen

{"title":"Improved Nonlinear Ultrasound Sensing based on 3D Printing Phonon Crystals","authors":"Wenkang Li, Hailin Cao, Liuyang Zhang, Xuefeng Chen","doi":"10.1109/ICSMD57530.2022.10058317","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058317","url":null,"abstract":"Nonlinear ultrasound is widely popularized by its high sensitivity to the micro-cracks and material damage at the early stages by sensing the second harmonic. However, the inherent nonlinear characteristics generated by equipment is the main bottleneck for making full use of nonlinear ultrasound. Here we adopt the phonon crystals plate as mechanical filter to eliminate the influence of the inherent nonlinear high harmonic. The metamaterial unit cell contains the PLA mass block and connective layer that can be fabricated by 3D printing. A strong resonance effect occurs between the mass block and Lamb waves and a stop band is opened due to the local resonance mechanism. On the contrary, the connective layer is used to make the array into single unit. Dispersion and transmission curves are calculated to analyze the bandgap theoretically. The experiments on the aluminum plate are carried out to verify the bandgap transmission and the elimination of inherent nonlinear high harmonic influence. The results show that phonon crystals can effectively suppress the influence of the inherent nonlinear harmonics and have great potential in simple, automatable, and accurate nonlinear ultrasound structural health monitoring and nondestructive testing applications.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125015077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Fault Detection Method for Dual-output Flyback Converters Using CCA 基于CCA的双输出反激变换器故障检测方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058460

Cuiyu Liu, Zhiming Yang, Gang Xiang, Yang Yu

{"title":"A Fault Detection Method for Dual-output Flyback Converters Using CCA","authors":"Cuiyu Liu, Zhiming Yang, Gang Xiang, Yang Yu","doi":"10.1109/ICSMD57530.2022.10058460","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058460","url":null,"abstract":"The flyback converter is highly preferred due to their cost effectiveness and electrical isolation characteristics. Because flyback converters are so crucial to the industrial world, it is crucial to assure their continuous and secure operation. A fault detection method based on CCA is suggested to efficiently identify a fault state for dual-output flyback converters. Firstly, both outputs voltage of the dual-output flyback converter are collected and then mean-centered. CCA is used to maximize the corelationship between the dual outputs. The residual matrix was constructed according to the correlation between the two outputs obtained by CCA. Then, a statistic is used to evaluate the residual matrix. Finally, calculate the corresponding threshold. The proposed method for detecting faults focuses on the correlation between the outputs, making it possible to identify faults with minimally abnormal characteristics. Fault detection in time can avoid further losses. Results from simulation experiments confirm the applicability and efficacy of the suggested method.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125160785","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Eddy Current Array for Defects Detection based on Spatiotemporal Self-attention Network 基于时空自关注网络的涡流阵列缺陷检测

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058281

Shouwei Gao, Yali Zheng, J. Zhang, L. Bai

{"title":"Eddy Current Array for Defects Detection based on Spatiotemporal Self-attention Network","authors":"Shouwei Gao, Yali Zheng, J. Zhang, L. Bai","doi":"10.1109/ICSMD57530.2022.10058281","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058281","url":null,"abstract":"Unlike single eddy current coil, eddy current array (ECA) which arranges multiple eddy current coils in a certain way, has the property of higher accuracy and efficiency to detect defects. The process of eddy current array collecting data own naturally spatial and temporal characteristics. In this paper, we introduce spatiotemporal self-attention mechanism to ECA Testing, and propose a spatiotemporal self-attention network for defect detection. In our framework, features from different channels are extracted separately and fused together by Downsampling Residual Attention Modules (DRAM) and Residual Attention Modules (RAM) in a pyramid manner, in which temporal attention module (TA) and spatial attention module (SA) are incorporated to capture spatiotemporally the features of defects. And the depth-wise and point-wise convolution are utilized to compute channel weights and spatial weights in TA and SA modules, respectively. Multiple channel data is taken as input from ECA, which finally leads to a classification result. The experimental results show that the proposed method not only outperforms the traditional image processing method significantly, but also is better than the state of the arts - ResNet, DenseNet in terms of F1 and accuracy.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126717663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Counterfactual Faithful Data Generation Based on Disentangled Representation for Compound Fault Diagnosis of Rolling Bearings 基于解纠缠表示的滚动轴承复合故障诊断反事实忠实数据生成

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058351

Xu Ding, Qiang Zhu, Song Wang, Yiqi Zhang, Hong Wang, Juan Xu

{"title":"Counterfactual Faithful Data Generation Based on Disentangled Representation for Compound Fault Diagnosis of Rolling Bearings","authors":"Xu Ding, Qiang Zhu, Song Wang, Yiqi Zhang, Hong Wang, Juan Xu","doi":"10.1109/ICSMD57530.2022.10058351","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058351","url":null,"abstract":"Recently, deep learning and human-out-of-the-loop methods enjoy their prosperous applications in mechanical fault diagnosis. Nonetheless, the None-IID(independent and identically distributed) issue radicated in acquired data severely limits the stability and accuracy of compound fault diagnosis of rolling bearings. This paper proposes a sample augmentation method for generating simulated signals based on the concept of counterfactuals. Firstly, disentangled representations and counterfactual faithful theory are applied to classify the original signal into two categories of properties. One is the fault semantics encoded from the original vibration signal. And the other is the sample attribute encoded by the encoder of Variational Autoencoders (VAEs). Secondly, the counterfactual faithful pseudo-samples are conjured through the Generative Adversarial Network(GAN) using the seeds of the “factual” sample attributes and “counterfactual” fault semantics to compensate for the drawback of distribution shift. Finally, the original samples and pseudo-samples are used as the CNN classifier dataset to realize bearing fault diagnosis. Experiments show that this method can generate counterfactual signals that are highly consistent with the original data distribution and can achieve better classification accuracy after balancing the dataset.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126932463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mob-YOLO: A Lightweight UAV Object Detection Method Mob-YOLO:一种轻型无人机目标检测方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058230

Yilin Liu, Datong Liu, Benkuan Wang, Bo Chen

{"title":"Mob-YOLO: A Lightweight UAV Object Detection Method","authors":"Yilin Liu, Datong Liu, Benkuan Wang, Bo Chen","doi":"10.1109/ICSMD57530.2022.10058230","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058230","url":null,"abstract":"With the increasing use of Unmanned Aerial Vehicles (UAVs) in various fields, the coordinated execution of tasks by multiple UAVs has become an important development trend in the future. To avoid the collision of multiple UAVs with each other during flight and ensure flight safety, it is essential to be able to achieve high-precision, real-time airborne UAV object detection. In this work, a UAV object detection method called Mob-YOLO is proposed. Based on the high-performance model YOLOv4, MobileNetv2, a lightweight convolutional neural network, is used to replace the original YOLOv4 backbone CSPDarknet53 for model size reduction and computing operation simplification. Meanwhile, to solve the issue of poor accuracy for small UAV objects after network replacement, this work also designs a multi-scale feature extraction and fusion branch to expand the receptive field of the object detector by multi-scale feature fusion. The proposed method is evaluated using a self-built UAV dataset. The results demonstrate that Mob-YOLO can satisfy accurate real-time monitoring of UAV objects, and the model size is tiny, which can be used for deployment on airborne embedded processors.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115123305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1