2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)最新文献_第6页

Research on PCB Small Target Defect Detection Based on Improved YOLOv5 基于改进YOLOv5的PCB小目标缺陷检测研究

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058458

M. Liang, Jigang Wu, Hong Cao

引用次数: 2

Research and Application of Intelligent Diagnosis Technology in Permanent Magnet Generator for Stress Demagnetization Fault 永磁发电机应力退磁故障智能诊断技术研究与应用

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058369

Nadeem Shahbaz, Yu Chen, Feng Liang, Shouwang Zhao, Sichao Zhang, Shuang Wang, Yong Ma, Yong Zhao, Weisi Deng

{"title":"Research and Application of Intelligent Diagnosis Technology in Permanent Magnet Generator for Stress Demagnetization Fault","authors":"Nadeem Shahbaz, Yu Chen, Feng Liang, Shouwang Zhao, Sichao Zhang, Shuang Wang, Yong Ma, Yong Zhao, Weisi Deng","doi":"10.1109/ICSMD57530.2022.10058369","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058369","url":null,"abstract":"Fault diagnosis before its existence and the complete shutdown is essentially critical for the whole industry. Fault diagnosis based on condition monitoring methods and artificial intelligence techniques are very potent. This paper assesses the machine-learning-based processes using air gap flux and stator current for eccentricity, magnet broken, and stator inter-turn short circuit faults in Permanent Magnet Generator (PMG). To apply machine learning, features are extracted via Discrete Wavelet Transform (DWT) technique for faulty and healthy conditions. Afterward, the classification learner toolbox in MATLAB is used to investigate various machine learning classifiers. The six fundamental classifiers comprising 23 sub-classifier algorithms are trained, whereby 16 out of 23 algorithms have achieved a perfect accuracy of (100 percent) while two have acquired an accuracy of more than 60 percent. The results indicate that air gap flux has performed better than stator current for fault diagnosis.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"6 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":"126571398","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

A Review of Wearable Sensor-based Human Activity Recognition using Deep Learning 基于穿戴式传感器的深度学习人体活动识别研究综述

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058422

Yaojie Zhu, L. Mo

{"title":"A Review of Wearable Sensor-based Human Activity Recognition using Deep Learning","authors":"Yaojie Zhu, L. Mo","doi":"10.1109/ICSMD57530.2022.10058422","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058422","url":null,"abstract":"Human activity recognition is an important direction in pattern recognition that learns from low-level raw signals acquired from smartphones and commercially available and customizable wearable devices to acquire high-level knowledge. HAR plays an essential role in providing smart healthcare to physically impaired older adults, with potential applications for elderly care, fall detection physical rehabilitation, clinical assessment and surveillance. Numerous researchers and scholars have conducted HAR based on conventional pattern recognition (PR) approaches and deep models. Conventional PR methods rely on the heuristic hand-crafted feature, which needs to pre-process the raw signals. Deep learning models can automatically learn features end-to-end, compared with the conventional PR approaches have achieved promising performance. Therefore, this paper reviews the progress of activity recognition based on wearable sensor devices, and discussed the potential application areas of human motion recognition technology. Finally, this paper discussed the related problems that can be further studied in the field of activity recognition.","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":"124441943","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

A portable multi-physiological signal synchronization acquisition device for cuff-less blood pressure variation tracking 一种用于无袖带血压变化跟踪的便携式多生理信号同步采集装置

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058406

Yumin Li, Zhijun Xiao, Feifei Chen, Chenxi Yang, Zhipeng Cai, Jianqing Li, Chengyu Liu

{"title":"A portable multi-physiological signal synchronization acquisition device for cuff-less blood pressure variation tracking","authors":"Yumin Li, Zhijun Xiao, Feifei Chen, Chenxi Yang, Zhipeng Cai, Jianqing Li, Chengyu Liu","doi":"10.1109/ICSMD57530.2022.10058406","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058406","url":null,"abstract":"Blood pressure (BP) is a vital physiological parameter of the human body. Currently, BP measurement needs to be portable and comfortable. Thus, cuff-less BP measurement is becoming more and more crucial. This paper proposes a portable multi-physiological signal synchronization acquisition device for cuff-less BP variation tracking. The analog front-end circuit is designed to record in Electrocardiogram (ECG), Photoplethysmogram (PPG), and Phonocardiogram (PCG). The effects of the pre-ejection period (PEP) can be reduced using PPG and PCG signals. After that, an artificially induced BP variation experiment is designed, and data is collected from 10 subjects to confirm the reliability of this device. Three pulse transit time indexes (PTT, PTTD, PTTV) is calculated from the PPG and PCG features. Finally, the Pearson correlation coefficients between the reference BP and three PTTs are calculated. The results show that PTTD had the highest correlation with SBP (−0.869 ± 0.153), and PTT had the highest correlation with DBP (−0.737 ± 0.117). Therefore, this device is feasible for tracking BP variations by a cuff-less method.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"15 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":"133647359","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

A Tensor Solution for Health Indicator Construction of Metro Wheelset Degradation with Irregular Noise 含不规则噪声地铁轮对退化健康指标构建的张量解

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058262

Yu Wang, Wentao Mao, Linlin Kou, Keying Liu

{"title":"A Tensor Solution for Health Indicator Construction of Metro Wheelset Degradation with Irregular Noise","authors":"Yu Wang, Wentao Mao, Linlin Kou, Keying Liu","doi":"10.1109/ICSMD57530.2022.10058262","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058262","url":null,"abstract":"During the operation of metro vehicles, wheel flange and wheel diameter continue to be abrased, so wheel degradation is inevitable. Influenced by the states of welded rail, subgrade and turnout, applied load, speed of running and other components, the degradation process of wheelset is vulnerable to irregular noise interference, which leads to abnormal fluctuation of vibration signal. As a result, the degradation process is hard to be accurately described. To address this problem, this paper proposes a new health indicator construction method for metro wheelset based on tensor reconstruction. First, tensor Tucker decomposition is utilized to obtain the core tensor of original signal, and then tensor reconstruction is applied to transform the signal into a new degradation sequence with noise reduction. Second, the Savitzky-Golay filter is employed to remove the irregular trend from the obtained degradation sequence. Finally, deep autoencoder network is used to extract deep degradation features, and after dimension reduction, a health indicator of wheelset degradation process can be obtained. The effectiveness of the proposed method is verified with Beijing subway wheelset degradation dataset in 2020. The results show that the constructed health indicator can accurately describe the whole degradation process with good trendability and monotonicity. More importantly, the proposed method possess good practicability since the key changing parts in the health indicator can correspond well to the actual maintenance records.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"20 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":"128379645","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

A New Incremental Training Framework Based on Dynamic Weight Allocation for Intelligent Fault Diagnosis 基于动态权重分配的智能故障诊断增量训练框架

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058355

Kui Hu, Qingbo He

{"title":"A New Incremental Training Framework Based on Dynamic Weight Allocation for Intelligent Fault Diagnosis","authors":"Kui Hu, Qingbo He","doi":"10.1109/ICSMD57530.2022.10058355","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058355","url":null,"abstract":"Intelligent fault diagnosis (IFD) has become a research primary concern in prognostic and health management. In engineering, it is hard to collect all fault mode data in advance. However, the existing IFD-trained models usually do not have the ability to continue to learn and expand. It is also costly to retrain IFD models when new fault modes data arrives. To solve this drawback, this paper proposes a new incremental training framework for IFD model updating. The framework increases the model's ability to diagnose new fault modes by adding new linear classification nodes to the original model. Cross-entropy and knowledge distillation loss are used to avoid catastrophic forgetting, and a new dynamic weight allocation strategy is introduced to solve the stability plasticity dilemma. Finally, stable and reliable incremental training and dynamic updating of the IFD model are realized. The proposed method is applied to incremental fault diagnosis of bearings. The results show that the IFD model applied with the proposed framework has high accuracy in incremental diagnosis tasks, which provides a new solution for the expansion of the IFD model.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"161 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":"131646696","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

Evaluation Method of Performance Degradation based on End Magnetic Flux Leakage for Short Circuit Fault of Doubly Fed Induction Generator 基于端漏磁的双馈感应发电机短路故障性能退化评价方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058220

Shouwang Zhao, Yu Chen, Feng Liang, Sichao Zhang, Nadeem Shahbaz, Shuang Wang, Yong Zhao, Wei Deng, Yonghong Cheng

{"title":"Evaluation Method of Performance Degradation based on End Magnetic Flux Leakage for Short Circuit Fault of Doubly Fed Induction Generator","authors":"Shouwang Zhao, Yu Chen, Feng Liang, Sichao Zhang, Nadeem Shahbaz, Shuang Wang, Yong Zhao, Wei Deng, Yonghong Cheng","doi":"10.1109/ICSMD57530.2022.10058220","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058220","url":null,"abstract":"The health status of the Doubly Fed Induction Generator (DFIG) is related to the actual operating conditions, the external environment, the accumulation of sudden factors, and the coupling effect. The degradation feature extraction is mainly based on a single signal or multiple statistics of a single signal. The principal component components were extracted from the end magnetic flux leakage (MFL) monitoring data of DFIG to evaluate performance degradation. This paper proposes an evaluation method of performance degradation for short circuit faults based on Variational Mode Decomposition (VMD) and Support Vector Data Description (SVDD). The process for detecting short circuit faults and performance degradation by monitoring the end-external MFL. For short circuits, when the magnetic flux leakage signal monitoring by the external environment, abnormal signal and noise problems, the VMD method is used to decompose the MFL signals and extract the most relevant modal composition of Root Mean Square (RMS), Singular Value Decomposition (SVD), Sample Entropy (SE), Refined Composite Multiscale Dispersion Entropy (RCMDE) as the main feature vectors. For a set of feature vector sets of MFL signal, then using the SVDD to perform performance degradation assessment. The distance between the sample data to be checked and the center of the trained hypersphere model is used to describe the performance degradation degree, and the membership function is used to transform the distance index into the membership degree of the normal state as the performance degradation index, to realize the state evaluation of the performance degradation degree of the generator.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"106 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114080244","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

A Sparse Autoencoder Based Adversarial Open Set Domain Adaptation Model for Fault Diagnosis of Rotating Machinery 基于稀疏自编码器的旋转机械故障诊断对抗开集域自适应模型

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058385

Zhaohua Liu, Lin-Bo Jiang, Hua-Liang Wei, Chang-Tong Wang, M. Lv, Lei Chen

{"title":"A Sparse Autoencoder Based Adversarial Open Set Domain Adaptation Model for Fault Diagnosis of Rotating Machinery","authors":"Zhaohua Liu, Lin-Bo Jiang, Hua-Liang Wei, Chang-Tong Wang, M. Lv, Lei Chen","doi":"10.1109/ICSMD57530.2022.10058385","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058385","url":null,"abstract":"Rotating machinery is an integral part of many industrial systems. Domain adaptation technique provides a powerful tool to detect faults under different working conditions. However, there is still a challenge: conventional domain adaptation approach only works under the ‘closed set’ assumption that all test classes are known at training time. In practice, a more realistic situation is ‘open set’, i.e., knowledge is incomplete in the training process, resulting in unknown classes during the testing. In this paper, a sparse autoencoder based adversarial open set domain adaptation (SAOSDA) model is proposed for rotating machinery fault diagnosis under open set scenarios, which can recognize the unknown faults and detect the known faults under different working conditions. This model utilizes adversarial learning to reduce the discrepancies between source samples and known target samples and reject the unknown target samples simultaneously. Experimental results of the actual bearing dataset verify the superiority and effectiveness of this method.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"8 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":"124533739","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

A Physics-based Approach to Reliability Modelling of the Shafting of Double-axis Turntable 基于物理的双轴转台轴系可靠性建模方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058379

Zhao Tao, Xiao-Yang Li, Xiang-Xiao Zhang, Wenbin Chen

引用次数: 0

Design of Data Acquisition Module Based on VME Bus 基于VME总线的数据采集模块设计

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058408

Hongtao Yin, Zhichao Chen

{"title":"Design of Data Acquisition Module Based on VME Bus","authors":"Hongtao Yin, Zhichao Chen","doi":"10.1109/ICSMD57530.2022.10058408","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058408","url":null,"abstract":"To meet the requirements of multi-channel measurement and equipment localization, a design of data acquisition module based on VME bus is designed. This paper introduces the function and design scheme of data acquisition module based on FPGA made in Chaina. From the functional division, the hardware circuit consists of signal acquisition circuit, data processing circuit and isolation circuit. The signal acquisition circuit includes signal conversion circuit and A/D converter circuit. Isolation circuit greatly reduces the crosstalk between analog and digital circuits, which is a key link to improve the measurement accuracy. FPGA is used to read, calibrate and store the digital data, and finally the data can be sent out through VME bus. In the aspect of data calibration, this paper elaborates. In order to complete the calibration function, the participation of computer software is also required. Finally, after measuring the data acquisition module in many aspects and analyzing the data, several possible causes of error are given. This verifies the effectiveness of the design of data acquisition module based on VME bus. Compared with the existing multi-channel measurement module, this module is convenient to use and has the advantage of being more stable and accurate in complex environments.","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":"115887530","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