Pub Date : 2025-10-13DOI: 10.1109/JSEN.2025.3618944
Hongwei Fan;Jiewen Gao;Xiangang Cao;Xuhui Zhang
Fault diagnosis of motor as a critical component in industrial systems plays a vital role in ensuring equipment safety and improving production efficiency. To address the challenge of weak signal characteristics under low rotational speed and load-fluctuation conditions, this article proposes a multi-modal feature fusion method that integrates time-domain features with frequencydomain graph features and an improved graph convolutional network and graph attention network fusion (GCN-GAT) fault diagnosis model based on graph neural networks (GNNs). Firstly, an adaptive K-nearest neighbor (KNN) graph construction method is introduced to build graph data based on frequency-domain information. Then, by improving the basic GNN architecture, a novel GCN-GAT model is developed to extract both local and global spatial features of graph nodes, with residual connections incorporated to improve model expressiveness and training stability. Key time-domain features are selected using a random forest (RF) algorithm, and an attention-based weighted fusion module is designed to adaptively integrate these time-domain features and frequency-domain graph features, thereby enhancing the model's adaptability to complex operating conditions. Experimental data were collected on a self-built test platform under normal conditions, mechanical faults of bearing and rotor, and electrical faults of stator and rotor, with load variations at speeds of 450, 900, and 1350 r/min, while data at 2250 r/min serve as a high rotational speed comparison item. Results demonstrate that the proposed model achieves high accuracy and robustness in motor fault diagnosis under low rotational speed loadfluctuation conditions, consistently exceeding an accuracy of 95%, which confirms the effectiveness and robustness of the proposed fault diagnosis method.
{"title":"A Novel Motor Fault Diagnosis Method Based on Adaptive Frequency-Domain Graph and Time-Domain Feature Fusion With GCN-GAT","authors":"Hongwei Fan;Jiewen Gao;Xiangang Cao;Xuhui Zhang","doi":"10.1109/JSEN.2025.3618944","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3618944","url":null,"abstract":"Fault diagnosis of motor as a critical component in industrial systems plays a vital role in ensuring equipment safety and improving production efficiency. To address the challenge of weak signal characteristics under low rotational speed and load-fluctuation conditions, this article proposes a multi-modal feature fusion method that integrates time-domain features with frequencydomain graph features and an improved graph convolutional network and graph attention network fusion (GCN-GAT) fault diagnosis model based on graph neural networks (GNNs). Firstly, an adaptive K-nearest neighbor (KNN) graph construction method is introduced to build graph data based on frequency-domain information. Then, by improving the basic GNN architecture, a novel GCN-GAT model is developed to extract both local and global spatial features of graph nodes, with residual connections incorporated to improve model expressiveness and training stability. Key time-domain features are selected using a random forest (RF) algorithm, and an attention-based weighted fusion module is designed to adaptively integrate these time-domain features and frequency-domain graph features, thereby enhancing the model's adaptability to complex operating conditions. Experimental data were collected on a self-built test platform under normal conditions, mechanical faults of bearing and rotor, and electrical faults of stator and rotor, with load variations at speeds of 450, 900, and 1350 r/min, while data at 2250 r/min serve as a high rotational speed comparison item. Results demonstrate that the proposed model achieves high accuracy and robustness in motor fault diagnosis under low rotational speed loadfluctuation conditions, consistently exceeding an accuracy of 95%, which confirms the effectiveness and robustness of the proposed fault diagnosis method.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 22","pages":"42334-42349"},"PeriodicalIF":4.3,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500464","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-13DOI: 10.1109/JSEN.2025.3618327
Xiaohu Zheng;Zhouzhi Gu
The blade, as a core component in modern industrial systems, exerts significant influence on the performance of both aeroengines and steam turbines through its inspection accuracy and efficiency. Blade inspection serves dual purposes: evaluating machining precision for error compensation and enabling failure diagnosis for expedited maintenance. This study proposes an electroforming-based planar coil sensor ($Phi 3.5 times 1.5~ text{mm}$ ) for key-point sampling, optimizing measurement efficiency. The sensor’s fabrication methodology is systematically detailed, and its efficacy is validated through numerical simulations and experimental trials. Results demonstrate >95% detection accuracy for defects of varying depths and geometries, with consistent response characteristics. Case studies confirm the sensor’s capability to reliably identify internal/external defects using minimal measurement points while sustaining realtime performance.
叶片作为现代工业系统的核心部件，其检测精度和效率对航空发动机和汽轮机的性能有着重要的影响。叶片检查有双重目的：评估加工精度以补偿误差，并使故障诊断能够加速维护。本研究提出了一种基于电成型的平面线圈传感器（$Phi 3.5 times 1.5~ text{mm}$）用于关键点采样，优化了测量效率。系统地阐述了传感器的制作方法，并通过数值模拟和实验验证了传感器的有效性。结果表明，对于不同深度和不同几何形状的缺陷，该方法的检测准确率为95%，且响应特性一致。案例研究证实了传感器在保持实时性能的同时，使用最小的测量点可靠地识别内部/外部缺陷的能力。
{"title":"Investigation of a Blade Inspection Method by Using Double Planar Coils","authors":"Xiaohu Zheng;Zhouzhi Gu","doi":"10.1109/JSEN.2025.3618327","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3618327","url":null,"abstract":"The blade, as a core component in modern industrial systems, exerts significant influence on the performance of both aeroengines and steam turbines through its inspection accuracy and efficiency. Blade inspection serves dual purposes: evaluating machining precision for error compensation and enabling failure diagnosis for expedited maintenance. This study proposes an electroforming-based planar coil sensor (<inline-formula> <tex-math>$Phi 3.5 times 1.5~ text{mm}$ </tex-math></inline-formula>) for key-point sampling, optimizing measurement efficiency. The sensor’s fabrication methodology is systematically detailed, and its efficacy is validated through numerical simulations and experimental trials. Results demonstrate >95% detection accuracy for defects of varying depths and geometries, with consistent response characteristics. Case studies confirm the sensor’s capability to reliably identify internal/external defects using minimal measurement points while sustaining realtime performance.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 22","pages":"42327-42333"},"PeriodicalIF":4.3,"publicationDate":"2025-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-08DOI: 10.1109/JSEN.2025.3617319
Kai Zhang;Yundan Liu;Yali Wang;Xiaowen Zhang
In the hot strip rolling mill (HSRM) process, accurate prediction and control of the strip crown are critical for quality assurance. In order to cope with this challenge, this study designed a real-time prediction and update system of strip crown based on the cloud-edgeend collaboration framework. First, this work optimizes the traditional variational autoencoder (VAE) network by refining the loss function structure to improve feature extraction and prediction, tailoring the VAE to the unique requirements of crown prediction. Second, according to the characteristics of multistand distribution in the HSRM process, a distributed framework is constructed to enable distributed extraction and fusion of crown-related features, generating predictions based on the fused features. In addition, to adapt to different strip specifications, a global and local update method is proposed to dynamically optimize model parameters, marking a notable advancement in adaptability for real-time industrial applications. The application results from two actual HSRM production lines (2150 and 1580 mm) demonstrate that the proposed method can decrease the prediction error to 2.650 $mu$ m on average. Finally, by using a cloud-edge-end prototype system with a 50-ms sampling interval, the system enables real-time prediction and supports online local model updates, significantly improving traditional methods while enhancing both operational efficiency and quality control.
{"title":"Application of Variational Autoencoder Network to Real-Time Prediction of Steel Crown in the Hot Strip Rolling Mill Process","authors":"Kai Zhang;Yundan Liu;Yali Wang;Xiaowen Zhang","doi":"10.1109/JSEN.2025.3617319","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3617319","url":null,"abstract":"In the hot strip rolling mill (HSRM) process, accurate prediction and control of the strip crown are critical for quality assurance. In order to cope with this challenge, this study designed a real-time prediction and update system of strip crown based on the cloud-edgeend collaboration framework. First, this work optimizes the traditional variational autoencoder (VAE) network by refining the loss function structure to improve feature extraction and prediction, tailoring the VAE to the unique requirements of crown prediction. Second, according to the characteristics of multistand distribution in the HSRM process, a distributed framework is constructed to enable distributed extraction and fusion of crown-related features, generating predictions based on the fused features. In addition, to adapt to different strip specifications, a global and local update method is proposed to dynamically optimize model parameters, marking a notable advancement in adaptability for real-time industrial applications. The application results from two actual HSRM production lines (2150 and 1580 mm) demonstrate that the proposed method can decrease the prediction error to 2.650 <inline-formula> <tex-math>$mu$ </tex-math></inline-formula>m on average. Finally, by using a cloud-edge-end prototype system with a 50-ms sampling interval, the system enables real-time prediction and supports online local model updates, significantly improving traditional methods while enhancing both operational efficiency and quality control.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 22","pages":"42389-42399"},"PeriodicalIF":4.3,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-06DOI: 10.1109/JSEN.2025.3615981
Shijian Dong;Tianyu Yu;Lixin Han;Jianguo Dong
To accurately predict the output of complex systems with input noise, a deep Informer network is innovatively designed, which combines signal decoupled denoising and interpretable functions. ELasticNet is employed for fitting evaluation and principal component feature selection. The dynamic variational mode decomposition (VMD) technique is established to decompose the input sequence. The high-frequency signal with a certain weight is combined with the low-frequency signal to realize decoupling reconstruction and weaken noise. The sliding window strategy is constructed to regularly decompose and update the newly obtained data online, so as to overcome the information leakage problem. Informer is applied to reasonably divide and reconstruct the principal component feature sequence. Encoder and decoder are used to realize feature capture under the embedding framework. In the encoder layer, the correlation of sequence signals is extracted and activated by multihead ProbSparse attention and wavelet activation function, respectively. The feedforward neural network (FNN) is utilized to map the extracted features by combining with the intermediate output of decoder. The combined results are analyzed globally using multihead attention. In the decoder layer, the masked attention and 1-D convolution are combined to decode features, and the fully connected layer is utilized to obtain the prediction output. The integrated gradient (IG) is applied to analyze the global and local interpretability of the prediction results to reveal the differential preferences of the proposed models in capturing key features. Finally, the accuracy and applicability of the proposed network are verified in complex industrial systems by comparing with the existing networks.
{"title":"Informer Network Fusing Interpretability and Dynamic Frequency Denoising Without Information Leakage for Predicting Complex Systems","authors":"Shijian Dong;Tianyu Yu;Lixin Han;Jianguo Dong","doi":"10.1109/JSEN.2025.3615981","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3615981","url":null,"abstract":"To accurately predict the output of complex systems with input noise, a deep Informer network is innovatively designed, which combines signal decoupled denoising and interpretable functions. ELasticNet is employed for fitting evaluation and principal component feature selection. The dynamic variational mode decomposition (VMD) technique is established to decompose the input sequence. The high-frequency signal with a certain weight is combined with the low-frequency signal to realize decoupling reconstruction and weaken noise. The sliding window strategy is constructed to regularly decompose and update the newly obtained data online, so as to overcome the information leakage problem. Informer is applied to reasonably divide and reconstruct the principal component feature sequence. Encoder and decoder are used to realize feature capture under the embedding framework. In the encoder layer, the correlation of sequence signals is extracted and activated by multihead ProbSparse attention and wavelet activation function, respectively. The feedforward neural network (FNN) is utilized to map the extracted features by combining with the intermediate output of decoder. The combined results are analyzed globally using multihead attention. In the decoder layer, the masked attention and 1-D convolution are combined to decode features, and the fully connected layer is utilized to obtain the prediction output. The integrated gradient (IG) is applied to analyze the global and local interpretability of the prediction results to reveal the differential preferences of the proposed models in capturing key features. Finally, the accuracy and applicability of the proposed network are verified in complex industrial systems by comparing with the existing networks.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 22","pages":"42372-42388"},"PeriodicalIF":4.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-06DOI: 10.1109/JSEN.2025.3615736
Yan-Lin He;Ze-Hao Bai;Yuan Xu;Qun-Xiong Zhu;Longchuan Li
The accurate detection of key quality variables plays a crucial role in process optimization and operational decision-making. As a result, real-time prediction of these variables is essential for effective monitoring and control in industrial processes. However, as sequence length and complexity increase, achieving accurate real-time predictions becomes more challenging. To address these challenges, this article proposes a novel time series prediction framework—patch decomposition enhanced temporal convolutional network with transformer (PETC-TNet), which combines a patch-based enhanced temporal convolutional network (TCN) with a Transformer architecture. PETC-TNet introduces a time-window block strategy that decomposes long sequences into manageable patches, preserving critical details. A channel attention mechanism is integrated into the TCN, forming the temporal convolutional channel attention network (TCCAN), which enhances feature extraction and improves the modeling of spatiotemporal relationships. The outputs from TCCAN are then processed by a Transformer module to effectively capture and attend to information across different historical time windows, overcoming the limitations of traditional Transformers with long sequences. Experiments on industrial datasets show that PETC-TNet surpasses Transformer-based and other state-of-the-art approaches in prediction accuracy, achieving notably lower mean absolute error (MAE). Additionally, sensitivity analysis reveals that PETC-TNet maintains reasonable sensitivity to sequence length and patch size, providing valuable insights for industrial soft sensor modeling.
{"title":"Patch-Decomposition-Enhanced TCN With Transformer for Soft Sensor Modeling","authors":"Yan-Lin He;Ze-Hao Bai;Yuan Xu;Qun-Xiong Zhu;Longchuan Li","doi":"10.1109/JSEN.2025.3615736","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3615736","url":null,"abstract":"The accurate detection of key quality variables plays a crucial role in process optimization and operational decision-making. As a result, real-time prediction of these variables is essential for effective monitoring and control in industrial processes. However, as sequence length and complexity increase, achieving accurate real-time predictions becomes more challenging. To address these challenges, this article proposes a novel time series prediction framework—patch decomposition enhanced temporal convolutional network with transformer (PETC-TNet), which combines a patch-based enhanced temporal convolutional network (TCN) with a Transformer architecture. PETC-TNet introduces a time-window block strategy that decomposes long sequences into manageable patches, preserving critical details. A channel attention mechanism is integrated into the TCN, forming the temporal convolutional channel attention network (TCCAN), which enhances feature extraction and improves the modeling of spatiotemporal relationships. The outputs from TCCAN are then processed by a Transformer module to effectively capture and attend to information across different historical time windows, overcoming the limitations of traditional Transformers with long sequences. Experiments on industrial datasets show that PETC-TNet surpasses Transformer-based and other state-of-the-art approaches in prediction accuracy, achieving notably lower mean absolute error (MAE). Additionally, sensitivity analysis reveals that PETC-TNet maintains reasonable sensitivity to sequence length and patch size, providing valuable insights for industrial soft sensor modeling.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 22","pages":"42364-42371"},"PeriodicalIF":4.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145500473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-06DOI: 10.1109/JSEN.2025.3613742
Ruiqin Zhao;Jinxia Li;Ting Shi;Haiyan Wang
Underwater acoustic sensor networks (UASNs) play a critical role in underwater communication and mission execution. However, their open nature and the dynamics of underwater acoustic channels (UACs) make them highly susceptible to spoofing attacks, posing severe security threats. Physical layer authentication (PLA) offers a promising defense by exploiting the unique characteristics of UACs, which are difficult to replicate. Nevertheless, most existing PLA schemes rely on static or statistical features that degrade significantly under time-varying ocean environments. To address this challenge, we propose a robust PLA (RPLA) scheme based on differential features designed for dynamic underwater channels. RPLA adopts a differential feature extraction method that compares each channel impulse response (CIR) with historical CIRs from the same link to quantify temporal variations. Five multidimensional differential features are extracted to capture fine-grained link variability and highlight distinctions between legitimate and adversarial links. These features are used to construct labeled training samples, which are then fed into an authentication model to enable robust and adaptive classification under time-varying underwater conditions. Extensive evaluations using both simulated and sea trial CIR datasets demonstrate that RPLA achieves high authentication accuracy and robust performance, significantly improving resistance to spoofing attacks. This work presents a practical and effective approach to enhancing physical layer security in dynamic underwater communication environments.
{"title":"Differential Feature-Based Physical Layer Authentication for Underwater Acoustic Sensor Networks","authors":"Ruiqin Zhao;Jinxia Li;Ting Shi;Haiyan Wang","doi":"10.1109/JSEN.2025.3613742","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3613742","url":null,"abstract":"Underwater acoustic sensor networks (UASNs) play a critical role in underwater communication and mission execution. However, their open nature and the dynamics of underwater acoustic channels (UACs) make them highly susceptible to spoofing attacks, posing severe security threats. Physical layer authentication (PLA) offers a promising defense by exploiting the unique characteristics of UACs, which are difficult to replicate. Nevertheless, most existing PLA schemes rely on static or statistical features that degrade significantly under time-varying ocean environments. To address this challenge, we propose a robust PLA (RPLA) scheme based on differential features designed for dynamic underwater channels. RPLA adopts a differential feature extraction method that compares each channel impulse response (CIR) with historical CIRs from the same link to quantify temporal variations. Five multidimensional differential features are extracted to capture fine-grained link variability and highlight distinctions between legitimate and adversarial links. These features are used to construct labeled training samples, which are then fed into an authentication model to enable robust and adaptive classification under time-varying underwater conditions. Extensive evaluations using both simulated and sea trial CIR datasets demonstrate that RPLA achieves high authentication accuracy and robust performance, significantly improving resistance to spoofing attacks. This work presents a practical and effective approach to enhancing physical layer security in dynamic underwater communication environments.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 21","pages":"40834-40848"},"PeriodicalIF":4.3,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-02DOI: 10.1109/JSEN.2025.3614813
Jowa Yangchin;Ningrinla Marchang
This article proposes the enhanced utility and reverse auction (EURA) framework as an incentive mechanism for mobile crowdsensing. EURA integrates reverse auction principles with utility optimization, forming an innovative region-based strategy that enhances data sensing efficiency and coverage maximization. Through an adaptive bidding model, EURA ensures fair and strategic participant selection, maintaining optimal resource allocation across large-scale sensing networks. EURA optimizes participation by assigning efficiencies based on users’ regions, fostering localized engagement and fair competition across diverse sensing environments. This article introduces a greedy incentive mechanism called EURA with greedy auction incentive (EGAIN) that dynamically adjusts bid evaluations based on data quality and regional significance, optimizing both competition fairness and efficiency. Additionally, the coverage-aware auction strategy mitigates redundancy while fostering an equitable distribution of sensing responsibilities. A variant model is also proposed called EURA with reputation auction incentive (ERAIN), incorporating reputation-based bid evaluations to further refine selection criteria and strengthen incentive alignment. Performance evaluations demonstrate EURA’s superiority in maximizing utility by 20%–50%, boosting participation by 30%–50% compared to RADP-VPC, Random, and RADP_EWMA while effectively minimizing bid exploitation and enabling cost efficient regional sensing, establishing its clear advantage over these existing mechanisms.
{"title":"Region-Based Incentive Mechanisms for Utility Maximization in Mobile Crowd Sensing","authors":"Jowa Yangchin;Ningrinla Marchang","doi":"10.1109/JSEN.2025.3614813","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3614813","url":null,"abstract":"This article proposes the enhanced utility and reverse auction (EURA) framework as an incentive mechanism for mobile crowdsensing. EURA integrates reverse auction principles with utility optimization, forming an innovative region-based strategy that enhances data sensing efficiency and coverage maximization. Through an adaptive bidding model, EURA ensures fair and strategic participant selection, maintaining optimal resource allocation across large-scale sensing networks. EURA optimizes participation by assigning efficiencies based on users’ regions, fostering localized engagement and fair competition across diverse sensing environments. This article introduces a greedy incentive mechanism called EURA with greedy auction incentive (EGAIN) that dynamically adjusts bid evaluations based on data quality and regional significance, optimizing both competition fairness and efficiency. Additionally, the coverage-aware auction strategy mitigates redundancy while fostering an equitable distribution of sensing responsibilities. A variant model is also proposed called EURA with reputation auction incentive (ERAIN), incorporating reputation-based bid evaluations to further refine selection criteria and strengthen incentive alignment. Performance evaluations demonstrate EURA’s superiority in maximizing utility by 20%–50%, boosting participation by 30%–50% compared to RADP-VPC, Random, and RADP_EWMA while effectively minimizing bid exploitation and enabling cost efficient regional sensing, establishing its clear advantage over these existing mechanisms.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 21","pages":"40861-40868"},"PeriodicalIF":4.3,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145405238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Non-contact blood pressure (BP) monitoring offers a comfortable and uninterrupted means of BP assessment, free from the constraints of physical contact. A core challenge in radar-based BP monitoring is the extraction of weak BP-related information from radar signals, which significantly affects both the accuracy and real-time performance of BP prediction models. To address this challenge, we focus on waveform features and temporal continuity, proposing a Temporal-Spatial Feature Fusion Network (TSFN) framework for radar-based BP prediction. The TSFN architecture integrates three components: Residual Networks (ResNet) for the extraction of detailed waveform features, gated recurrent units (GRUs) for capturing continuous temporal dependencies, and multiple head attention (MHA) to focus on critical information. To enhance the model’s robustness, a Pseudo–Huber loss function was employed to refine the optimization process, providing a smoother gradient transition and improved stability. Evaluations demonstrated impressive accuracies, with mean errors (MEs) of 0.24 ± 6.78 mmHg for systolic BP (SBP) and 0.25 ± 5.13 mmHg for diastolic BP (DBP). These outcomes meet the standards set by the British Hypertension Society (BHS) for grade “A” benchmarks for SBP and DBP measurements. Notably, the TSFN model avoids the need for complex feature engineering, demonstrating its effectiveness in monitoring BP fluctuations across diverse physiological states at 2 s intervals. This feature highlights its potential applicability in real-time monitoring systems. Furthermore, using our proposed TSFN framework, we have validated various combinations of temporal and spatial feature extraction networks. Our findings promise a significant advancement for continuous, non-contact BP monitoring with radar technology.
{"title":"A Temporal–Spatial Feature Fusion Network for Accurate Non-Contact Blood Pressure Measurement via Radar","authors":"Pengfei Wang;Hongqiu Zhang;Minghao Yang;Jianqi Wang;Cong Wang;Hongbo Jia","doi":"10.1109/JSEN.2025.3614579","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3614579","url":null,"abstract":"Non-contact blood pressure (BP) monitoring offers a comfortable and uninterrupted means of BP assessment, free from the constraints of physical contact. A core challenge in radar-based BP monitoring is the extraction of weak BP-related information from radar signals, which significantly affects both the accuracy and real-time performance of BP prediction models. To address this challenge, we focus on waveform features and temporal continuity, proposing a Temporal-Spatial Feature Fusion Network (TSFN) framework for radar-based BP prediction. The TSFN architecture integrates three components: Residual Networks (ResNet) for the extraction of detailed waveform features, gated recurrent units (GRUs) for capturing continuous temporal dependencies, and multiple head attention (MHA) to focus on critical information. To enhance the model’s robustness, a Pseudo–Huber loss function was employed to refine the optimization process, providing a smoother gradient transition and improved stability. Evaluations demonstrated impressive accuracies, with mean errors (MEs) of 0.24 ± 6.78 mmHg for systolic BP (SBP) and 0.25 ± 5.13 mmHg for diastolic BP (DBP). These outcomes meet the standards set by the British Hypertension Society (BHS) for grade “A” benchmarks for SBP and DBP measurements. Notably, the TSFN model avoids the need for complex feature engineering, demonstrating its effectiveness in monitoring BP fluctuations across diverse physiological states at 2 s intervals. This feature highlights its potential applicability in real-time monitoring systems. Furthermore, using our proposed TSFN framework, we have validated various combinations of temporal and spatial feature extraction networks. Our findings promise a significant advancement for continuous, non-contact BP monitoring with radar technology.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 21","pages":"40748-40762"},"PeriodicalIF":4.3,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145455803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-02DOI: 10.1109/JSEN.2025.3614730
Ayhan Yazgan;Ufuk Koçbıyık
Abstract-This article focuses on the wireless monitoring of rubber lifter-bar wear, which has been used for years in mills to grind ore under harsh environmental conditions. Due to the abrasive nature of the process, worn lifter-bars must be replaced after a certain period to prevent damage to the mill body, which is extremely costly. Since predicting this wear in advance is challenging, replacements often occur at incorrect times, leading to financial losses in the mining industry. In addition, lifter-bars that are not fully worn are frequently discarded, resulting in unnecessary waste. In this study, two partially conductive resistive sensor probes (RSPs) were designed and embedded into the lifter-bar. The resistance between the RSP terminals becomes part of a proposed modified relaxation oscillator. Due to the applied electric field and the presence of carbon black within the lifter-bar, an electric current related to the degree of wear flows between the RSP terminals, causing the oscillator’s frequency to vary accordingly. A microprocessor-based electronic circuit was developed to convert this frequency into digital wear data. The sensor board contains a transceiver operating at 2.4 GHz with a receiver sensitivity better than -120 dBm. The sensor circuit and antenna are located in a safe area of the lifter-bar, away from the wear zone, for wireless wear monitoring. The proposed sensor was installed on a commercial lifter-bar in an operational grinding mill located in Bingöl, Türkiye. To verify its reliability, battery power planning was conducted based on the proposed data packet structure, and wear data were monitored over a 100 -day period. Despite the thick metallic structure of the mill and the presence of hundreds of rotating metal balls inside, the wireless sensor successfully transmitted signals at -104 dBm with a $2.8-mathrm{dB}$ signal-to-noise ratio (SNR) outside the mill, achieving a $6 %$ wear resolution. Simulations and experimental results showed strong agreement with the theoretical model.
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