Pub Date : 2025-01-29DOI: 10.1109/TIM.2025.3528156
Sara Sulis;Alessandro Mingotti
{"title":"Guest Editors’ Foreword Special section on the 13th IEEE International Workshop on Applied Measurements for Power Systems (AMPS 2023)","authors":"Sara Sulis;Alessandro Mingotti","doi":"10.1109/TIM.2025.3528156","DOIUrl":"https://doi.org/10.1109/TIM.2025.3528156","url":null,"abstract":"","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-2"},"PeriodicalIF":5.6,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857466","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Thrust is one of the key parameters that determines the performance of thrusters in the propulsion systems of microsatellites. Thrusters are widely used in space exploration, quantum communication, and other fields. At present, the study on thrust measurement under low load has made great progress, meeting the requirements of high precision. However, it is still challenging to achieve high precision under heavy load. In this article, a flexible inverted pendulum based on multiple elliptic flexure hinges is proposed to achieve high-accuracy thrust measurement under heavy load. The theoretical model between sensitivity, stability, and pendulum parameters under heavy load is established. The principle that the pendulum can balance heavy load and high accuracy is explained as well. The pendulum is calibrated using precision machined electrostatic combs. The performance of the pendulum was evaluated by a cold gas thruster and a Hall thruster. Experimental results show that the resolution of the pendulum is better than $0.3~mu $ N and the measurement range covers 0.3–$1300~mu $ N for loads up to 4 kg.
{"title":"A Flexible Inverted Pendulum Based on Multiple Flexure Hinges for Microthrust Measurement Under Heavy Load","authors":"Chunyuan Zhu;Shixu Lu;Clarence TH Augustine Tee;Shiying Wen;Dan Kang;Congyun Chen;Meirong Zhao;Ning Guo;Yelong Zheng","doi":"10.1109/TIM.2025.3535566","DOIUrl":"https://doi.org/10.1109/TIM.2025.3535566","url":null,"abstract":"Thrust is one of the key parameters that determines the performance of thrusters in the propulsion systems of microsatellites. Thrusters are widely used in space exploration, quantum communication, and other fields. At present, the study on thrust measurement under low load has made great progress, meeting the requirements of high precision. However, it is still challenging to achieve high precision under heavy load. In this article, a flexible inverted pendulum based on multiple elliptic flexure hinges is proposed to achieve high-accuracy thrust measurement under heavy load. The theoretical model between sensitivity, stability, and pendulum parameters under heavy load is established. The principle that the pendulum can balance heavy load and high accuracy is explained as well. The pendulum is calibrated using precision machined electrostatic combs. The performance of the pendulum was evaluated by a cold gas thruster and a Hall thruster. Experimental results show that the resolution of the pendulum is better than <inline-formula> <tex-math>$0.3~mu $ </tex-math></inline-formula>N and the measurement range covers 0.3–<inline-formula> <tex-math>$1300~mu $ </tex-math></inline-formula>N for loads up to 4 kg.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/TIM.2025.3533644
Yunyi Jiang;Zhijun Xiao;Yuwei Zhang;Caiyun Ma;Chenxi Yang;Weiming Jin;Jianqing Li;Chengyu Liu
Noncontact capacitive electrocardiogram (cECG) is gaining recognition in cardiovascular disease monitoring for its comfort and noninvasiveness. Compared to the conventional electrocardiogram (ECG), cECG signal quality is prone to degradation in practical applications due to motion artifacts and power line interference (PLI). This study proposed an optimized signal quality assessment method to identify and remove low-quality cECG signals. First, the human body-electrode interface is modeled to analyze the generation mechanism and influence of cECG motion artifacts and PLI. Then, distinct signal quality indices (SQIs) are proposed to target the characteristics of these interferences. Moreover, optimized cECG features and previously proposed ECG features were combined as multifeatures and presented to XGBoost for binary classification training. Finally, Shapley additive explanations (SHAPs) were utilized for feature optimization to reduce redundant information. Validation on a labeled noncontact cECG database yields an impressive binary classification accuracy of 98.786%, an ${F}1$ -score of 98.845%, and a kappa of 97.567%. Moreover, its performance on a subject-independent validation set is also excellent, with an accuracy of 99.130%, an ${F}1$ -score of 96.937%, and a kappa of 96.430%. The optimized multifeatures also demonstrate favorable performance in a triple classification model. The experimental results show that our method offers a precise and convenient solution for cECG signal quality assessment.
{"title":"An Optimized Signal Quality Assessment Method for Noncontact Capacitive ECG","authors":"Yunyi Jiang;Zhijun Xiao;Yuwei Zhang;Caiyun Ma;Chenxi Yang;Weiming Jin;Jianqing Li;Chengyu Liu","doi":"10.1109/TIM.2025.3533644","DOIUrl":"https://doi.org/10.1109/TIM.2025.3533644","url":null,"abstract":"Noncontact capacitive electrocardiogram (cECG) is gaining recognition in cardiovascular disease monitoring for its comfort and noninvasiveness. Compared to the conventional electrocardiogram (ECG), cECG signal quality is prone to degradation in practical applications due to motion artifacts and power line interference (PLI). This study proposed an optimized signal quality assessment method to identify and remove low-quality cECG signals. First, the human body-electrode interface is modeled to analyze the generation mechanism and influence of cECG motion artifacts and PLI. Then, distinct signal quality indices (SQIs) are proposed to target the characteristics of these interferences. Moreover, optimized cECG features and previously proposed ECG features were combined as multifeatures and presented to XGBoost for binary classification training. Finally, Shapley additive explanations (SHAPs) were utilized for feature optimization to reduce redundant information. Validation on a labeled noncontact cECG database yields an impressive binary classification accuracy of 98.786%, an <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula>-score of 98.845%, and a kappa of 97.567%. Moreover, its performance on a subject-independent validation set is also excellent, with an accuracy of 99.130%, an <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula>-score of 96.937%, and a kappa of 96.430%. The optimized multifeatures also demonstrate favorable performance in a triple classification model. The experimental results show that our method offers a precise and convenient solution for cECG signal quality assessment.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/TIM.2025.3533618
Sheng Dai;Ming Li;Xu Wu;Xiangyu Ju;Xinyu Li;Jun Yang;Dewen Hu
Emotion recognition based on electroencephalography (EEG) has demonstrated promising effectiveness in recent years. However, challenges have been experienced, such as limited dataset availability, experimental protocol inconsistencies, and inherent spatiotemporal redundancies in the EEG data. In this work, we introduce a novel method of contrastive learning of EEG representation of brain area (CLRA). Our method is based on the fact that the EEG signals are of high similarity within brain regions and show significant differences between brain regions. The model is designed to obtain the representation capable of distinguishing signals from different brain areas. Specifically, a 1-D convolutional neural network (CNN) and a recurrent network were applied to learn temporal representations from channelwise EEG in contrastive learning. The representations were recombined and fused to extract features for emotion classification. Experimental evaluations performed on public database for emotion analysis using physiological signals (DEAP) and Shanghai Jiao Tong University emotion EEG dataset (SEED) demonstrate the efficacy of our proposed framework, yielding state-of-the-art results in EEG-based emotion recognition tasks. In our cross-subject experiment, our method achieved an accuracy of 95.23% and 96.31% in valence and arousal on the DEAP, and an accuracy of 95.16% on SEED. Additionally, our experiments involving reduced channel configurations demonstrated an improvement in classification accuracy even with fewer electrodes. Furthermore, CLRA exhibits strong generalization performance and robustness, facilitated by its ability to extract informative single-channel features, thus enabling seamless cross-dataset integration and training.
{"title":"Contrastive Learning of EEG Representation of Brain Area for Emotion Recognition","authors":"Sheng Dai;Ming Li;Xu Wu;Xiangyu Ju;Xinyu Li;Jun Yang;Dewen Hu","doi":"10.1109/TIM.2025.3533618","DOIUrl":"https://doi.org/10.1109/TIM.2025.3533618","url":null,"abstract":"Emotion recognition based on electroencephalography (EEG) has demonstrated promising effectiveness in recent years. However, challenges have been experienced, such as limited dataset availability, experimental protocol inconsistencies, and inherent spatiotemporal redundancies in the EEG data. In this work, we introduce a novel method of contrastive learning of EEG representation of brain area (CLRA). Our method is based on the fact that the EEG signals are of high similarity within brain regions and show significant differences between brain regions. The model is designed to obtain the representation capable of distinguishing signals from different brain areas. Specifically, a 1-D convolutional neural network (CNN) and a recurrent network were applied to learn temporal representations from channelwise EEG in contrastive learning. The representations were recombined and fused to extract features for emotion classification. Experimental evaluations performed on public database for emotion analysis using physiological signals (DEAP) and Shanghai Jiao Tong University emotion EEG dataset (SEED) demonstrate the efficacy of our proposed framework, yielding state-of-the-art results in EEG-based emotion recognition tasks. In our cross-subject experiment, our method achieved an accuracy of 95.23% and 96.31% in valence and arousal on the DEAP, and an accuracy of 95.16% on SEED. Additionally, our experiments involving reduced channel configurations demonstrated an improvement in classification accuracy even with fewer electrodes. Furthermore, CLRA exhibits strong generalization performance and robustness, facilitated by its ability to extract informative single-channel features, thus enabling seamless cross-dataset integration and training.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/TIM.2025.3529580
Hongxin Zhang;Teng Ran;Wendong Xiao;Kai Lv;Song Peng;Liang Yuan;Jingchuan Wang
Low-light images affect human perception and vision tasks because of low brightness, loss of details, and severe noise. Most existing methods adopt a multibranch structure with a refusion strategy to solve different image defects separately. However, the correlation between the multi-scale information of images has always been ignored, and the ability of multifeature fusion needs to be improved. In the article, we propose a multi-scale progressive fusion network to obtain feature representation by interacting with different resolution information. Concretely, sampling blocks based on dual-channel superposition are used to acquire different resolution features. We propose a feature fusion block that utilizes local perception and linear correlation to exchange information across resolution layers. An enhancement block based on depth and cyclic residual features is presented to improve brightness and details and suppress noise in different resolution layers. In addition, we introduce a set of loss functions to optimize the model parameters. The proposed method performs better on public datasets and real scenarios.
{"title":"Multi-Scale Progressive Fusion Network for Low-Light Image Enhancement","authors":"Hongxin Zhang;Teng Ran;Wendong Xiao;Kai Lv;Song Peng;Liang Yuan;Jingchuan Wang","doi":"10.1109/TIM.2025.3529580","DOIUrl":"https://doi.org/10.1109/TIM.2025.3529580","url":null,"abstract":"Low-light images affect human perception and vision tasks because of low brightness, loss of details, and severe noise. Most existing methods adopt a multibranch structure with a refusion strategy to solve different image defects separately. However, the correlation between the multi-scale information of images has always been ignored, and the ability of multifeature fusion needs to be improved. In the article, we propose a multi-scale progressive fusion network to obtain feature representation by interacting with different resolution information. Concretely, sampling blocks based on dual-channel superposition are used to acquire different resolution features. We propose a feature fusion block that utilizes local perception and linear correlation to exchange information across resolution layers. An enhancement block based on depth and cyclic residual features is presented to improve brightness and details and suppress noise in different resolution layers. In addition, we introduce a set of loss functions to optimize the model parameters. The proposed method performs better on public datasets and real scenarios.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/TIM.2025.3534224
Yanyan Shi;Luanjun Wang;Meng Wang;Bin Yang;Meng Dai;Feng Fu
In the dynamic monitoring with electrical impedance tomography (EIT), some unavoidable factors lead to electrode disconnection. Failure data are measured which greatly affects image reconstruction quality. To enhance the accuracy of lung imaging in the presence of electrode disconnection, this work presents a novel failure data correction approach based on shallow convolutional neural network (sCNN). Electrode disconnection is first identified by calculating the average relative change in the measured voltage. Then the method is applied for failure data correction caused by the disconnected electrode. The performance of the proposed method when the electrode is disconnected is evaluated by comparing the predicted data with the normal data. It is found that mean relative boundary voltage variation when the proposed method is used is very similar to the normal case. Besides, the deviation rate of the predicted voltage data approximates 0. Furthermore, image reconstruction of conductivity distribution is investigated for five different models, and disconnection of one electrode and two electrodes are considered. Also, we have tested the robustness of the proposed method to noise interruption. Both quantitative and qualitative evaluations show that reconstructed images are much better when the voltage data corrected by the proposed method is used for image reconstruction. The shape and size of the reconstructed lung are basically the same with the true object. In addition, there are almost no artifacts. To further estimate the proposed method, a phantom experimental validation is carried out. This work offers a choice for accurate image reconstruction of conductivity distribution under electrode disconnection in the lung EIT.
{"title":"Correction of Failure Data Under Electrode Disconnection for Accurate Electrical Impedance Tomography","authors":"Yanyan Shi;Luanjun Wang;Meng Wang;Bin Yang;Meng Dai;Feng Fu","doi":"10.1109/TIM.2025.3534224","DOIUrl":"https://doi.org/10.1109/TIM.2025.3534224","url":null,"abstract":"In the dynamic monitoring with electrical impedance tomography (EIT), some unavoidable factors lead to electrode disconnection. Failure data are measured which greatly affects image reconstruction quality. To enhance the accuracy of lung imaging in the presence of electrode disconnection, this work presents a novel failure data correction approach based on shallow convolutional neural network (sCNN). Electrode disconnection is first identified by calculating the average relative change in the measured voltage. Then the method is applied for failure data correction caused by the disconnected electrode. The performance of the proposed method when the electrode is disconnected is evaluated by comparing the predicted data with the normal data. It is found that mean relative boundary voltage variation when the proposed method is used is very similar to the normal case. Besides, the deviation rate of the predicted voltage data approximates 0. Furthermore, image reconstruction of conductivity distribution is investigated for five different models, and disconnection of one electrode and two electrodes are considered. Also, we have tested the robustness of the proposed method to noise interruption. Both quantitative and qualitative evaluations show that reconstructed images are much better when the voltage data corrected by the proposed method is used for image reconstruction. The shape and size of the reconstructed lung are basically the same with the true object. In addition, there are almost no artifacts. To further estimate the proposed method, a phantom experimental validation is carried out. This work offers a choice for accurate image reconstruction of conductivity distribution under electrode disconnection in the lung EIT.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388601","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/TIM.2025.3534226
Hui Zhang;Zhaoyu Zong;Xiaobo Rui;Si Liu
During the operational lifespan of the honeycomb sandwich structure (HSS), it is necessary to monitor the structural integrity to ensure its safety. Air-coupled ultrasonic guided wave technology is an efficient and convenient noncontact detection method. Due to the large acoustic impedance difference between the air and the structure and the dispersion characteristics of guided wave in the HSS, the air-coupled signals lead to significant energy loss and are easily stacked, posing challenges for the detection of debonding defects. To tackle this challenge, this study investigates the dispersion characteristics and propagation properties of guided waves in the HSS, and the effect of debonding defects on guided wave signals is researched by finite element models. The proposed linear mapping dispersion compensation algorithm refactors the linearization dispersion relationship in the frequency domain of air-coupled guided wave signals. It effectively reconstructs the stacked signal and separates the direct wave packet. Damage probability imaging is realized by using the amplitude of the direct wave to construct the damage index (DI). The imaging evaluation indexes of intersection over union (IoU) and recall rate for debonding defects of two sizes are compared, which demonstrates an improvement in defect detection accuracy. The proposed method has strong potential for real-time monitoring applications.
{"title":"A Linear Mapping Dispersion Compensation Method for Debonding Detection of Honeycomb Sandwich Structure Based on Air-Coupled Ultrasound","authors":"Hui Zhang;Zhaoyu Zong;Xiaobo Rui;Si Liu","doi":"10.1109/TIM.2025.3534226","DOIUrl":"https://doi.org/10.1109/TIM.2025.3534226","url":null,"abstract":"During the operational lifespan of the honeycomb sandwich structure (HSS), it is necessary to monitor the structural integrity to ensure its safety. Air-coupled ultrasonic guided wave technology is an efficient and convenient noncontact detection method. Due to the large acoustic impedance difference between the air and the structure and the dispersion characteristics of guided wave in the HSS, the air-coupled signals lead to significant energy loss and are easily stacked, posing challenges for the detection of debonding defects. To tackle this challenge, this study investigates the dispersion characteristics and propagation properties of guided waves in the HSS, and the effect of debonding defects on guided wave signals is researched by finite element models. The proposed linear mapping dispersion compensation algorithm refactors the linearization dispersion relationship in the frequency domain of air-coupled guided wave signals. It effectively reconstructs the stacked signal and separates the direct wave packet. Damage probability imaging is realized by using the amplitude of the direct wave to construct the damage index (DI). The imaging evaluation indexes of intersection over union (IoU) and recall rate for debonding defects of two sizes are compared, which demonstrates an improvement in defect detection accuracy. The proposed method has strong potential for real-time monitoring applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-27DOI: 10.1109/TIM.2025.3534227
Ningning Yang;Wei Zhang;Jingqi Zhang;Ke Wang;Yin Su;Yunpeng Liu
In the field of remaining useful life (RUL) prediction, accurately evaluating incipient faults in bearings by using conventional health indicators (HIs) poses challenges, while traditional neural network models fail to provide reliable uncertainty distributions for credible output. Therefore, a cutting-edge deep learning (DL) method based on fault feature gain (FFG) is proposed, which aims to accurately predict the RUL of rolling bearings while quantifying the associated uncertainty distribution. First, combined with the adaptive spectrum mode extraction (ASME) theory, FFG is proposed to quantitatively assess the degree of bearing damage. Second, a mechanism for identifying incipient faults is established to determine the optimal time for making the first prediction. Subsequently, a DL model combining gated recurrent unit (GRU) and Bayesian neural network (BNN) is constructed to predict the RUL of bearings and quantify the uncertainty distribution. Finally, experimental results obtained from an accelerated degradation test bench for rolling bearings validate the effectiveness and advantages of the proposed method. The results demonstrate that FFG enables accurate assessment of bearing health status while providing crucial insights into the underlying failure modes. Furthermore, the GRU-BNN model performs more accurately in RUL prediction and can better quantify the uncertainty of RUL.
{"title":"A Method for Remaining Useful Life Prediction and Uncertainty Quantification of Rolling Bearings Based on Fault Feature Gain","authors":"Ningning Yang;Wei Zhang;Jingqi Zhang;Ke Wang;Yin Su;Yunpeng Liu","doi":"10.1109/TIM.2025.3534227","DOIUrl":"https://doi.org/10.1109/TIM.2025.3534227","url":null,"abstract":"In the field of remaining useful life (RUL) prediction, accurately evaluating incipient faults in bearings by using conventional health indicators (HIs) poses challenges, while traditional neural network models fail to provide reliable uncertainty distributions for credible output. Therefore, a cutting-edge deep learning (DL) method based on fault feature gain (FFG) is proposed, which aims to accurately predict the RUL of rolling bearings while quantifying the associated uncertainty distribution. First, combined with the adaptive spectrum mode extraction (ASME) theory, FFG is proposed to quantitatively assess the degree of bearing damage. Second, a mechanism for identifying incipient faults is established to determine the optimal time for making the first prediction. Subsequently, a DL model combining gated recurrent unit (GRU) and Bayesian neural network (BNN) is constructed to predict the RUL of bearings and quantify the uncertainty distribution. Finally, experimental results obtained from an accelerated degradation test bench for rolling bearings validate the effectiveness and advantages of the proposed method. The results demonstrate that FFG enables accurate assessment of bearing health status while providing crucial insights into the underlying failure modes. Furthermore, the GRU-BNN model performs more accurately in RUL prediction and can better quantify the uncertainty of RUL.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-01-24DOI: 10.1109/TIM.2025.3533658
Zilan Li;Zhibin Zou;Weiliang Xu;Yuanzhi Zhou;Guoyuan Zhou;Muxing Huang;Xuan Huang;Xinming Li
Vision-based tactile sensors encode object data via optical signals, capturing microscale deformations using elastomer through densely arranged optical imaging sensors to detect subtle data variations. To enable continuous contact recognition, elastomers are crafted with curved surfaces to adjust to changes in the contact area. However, this design leads to uneven deformations, distorting tactile images and inaccurately reflecting the true elastomer deformations. In this work, we propose a cyclic fusion strategy for vision-based tactile sensing for precise contact data extraction and shape feature integration at the pixel level. Utilizing frequency-domain fusion, the system merges topography as indicated by elastomer deformation, enhancing information content by 8%, and regional information bias is reduced by 20% when preserving structural consistency. Furthermore, this system could effectively extract and summarize microscale contact features, decreasing erroneous predictions by 20% in defect detection via neural networks and reducing surface projection bias by 50% in surface depth reconstruction. Using this strategy, the measurement minimizes data interference, accurately depicting object morphology on tactile images and enhancing tactile sensation restoration.
{"title":"Cyclic Fusion of Measuring Information in Curved Elastomer Contact via Vision-Based Tactile Sensing","authors":"Zilan Li;Zhibin Zou;Weiliang Xu;Yuanzhi Zhou;Guoyuan Zhou;Muxing Huang;Xuan Huang;Xinming Li","doi":"10.1109/TIM.2025.3533658","DOIUrl":"https://doi.org/10.1109/TIM.2025.3533658","url":null,"abstract":"Vision-based tactile sensors encode object data via optical signals, capturing microscale deformations using elastomer through densely arranged optical imaging sensors to detect subtle data variations. To enable continuous contact recognition, elastomers are crafted with curved surfaces to adjust to changes in the contact area. However, this design leads to uneven deformations, distorting tactile images and inaccurately reflecting the true elastomer deformations. In this work, we propose a cyclic fusion strategy for vision-based tactile sensing for precise contact data extraction and shape feature integration at the pixel level. Utilizing frequency-domain fusion, the system merges topography as indicated by elastomer deformation, enhancing information content by 8%, and regional information bias is reduced by 20% when preserving structural consistency. Furthermore, this system could effectively extract and summarize microscale contact features, decreasing erroneous predictions by 20% in defect detection via neural networks and reducing surface projection bias by 50% in surface depth reconstruction. Using this strategy, the measurement minimizes data interference, accurately depicting object morphology on tactile images and enhancing tactile sensation restoration.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The infrared thermography (IRT) is a prevalent noncontact approach for measuring temperature fields. However, the dynamic water mist can absorb and scatter infrared radiation, resulting in measurement inaccuracies. Addressing this issue, a novel temperature field measurement method based on transmittance field estimation under water mist interference is proposed, and the key point is to introduce visible vision to obtain prior environmental information. First, a visible and thermal image registration algorithm are designed to solve the unaligned images, which incorporates camera imaging parameters to constrain the affine parameters. Then, a two-stage transmittance field estimation model combining visible and infrared vision is established to quantify the interference of water mist into transmittance. Following this, based on the principle of infrared temperature measurement, a temperature field measurement model tailored for the water mist environment is constructed, and the temperature field is accurately measured by substituting the estimated transmittance field. Finally, the experimental results with five common objects and four different heating plates show that the proposed method can achieve accurate temperature field measurement under the interference of dynamic water mist.
{"title":"A Novel Noncontact Temperature Field Measurement Method Based on Transmittance Field Estimation Under Dynamic Water Mist Interference","authors":"Yitian Li;Dong Pan;Zhaohui Jiang;Haoyang Yu;Weihua Gui","doi":"10.1109/TIM.2025.3533633","DOIUrl":"https://doi.org/10.1109/TIM.2025.3533633","url":null,"abstract":"The infrared thermography (IRT) is a prevalent noncontact approach for measuring temperature fields. However, the dynamic water mist can absorb and scatter infrared radiation, resulting in measurement inaccuracies. Addressing this issue, a novel temperature field measurement method based on transmittance field estimation under water mist interference is proposed, and the key point is to introduce visible vision to obtain prior environmental information. First, a visible and thermal image registration algorithm are designed to solve the unaligned images, which incorporates camera imaging parameters to constrain the affine parameters. Then, a two-stage transmittance field estimation model combining visible and infrared vision is established to quantify the interference of water mist into transmittance. Following this, based on the principle of infrared temperature measurement, a temperature field measurement model tailored for the water mist environment is constructed, and the temperature field is accurately measured by substituting the estimated transmittance field. Finally, the experimental results with five common objects and four different heating plates show that the proposed method can achieve accurate temperature field measurement under the interference of dynamic water mist.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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