In this article, we propose an optical sensor system utilizing the capabilities of hollow-core photonic crystal fiber (HC-PCF) to transmit excitation light and hold sample gas. The direct absorption response is examined for different modulation waveforms, pressures, frequencies, and concentrations. The experimental results show that the response signal has an obvious overshoot and spike. The overshoot and spike are more obvious under the sine modulation than the rectangular modulation. In the 1–500-Hz range, the system demonstrated optimum response characteristics at 5 Hz, corresponding to the maximum attenuation percentage. Furthermore, a positive correlation was observed between the attenuation percentage of light intensity (${A} _{text {t}}$ ) and optical power (${A} _{text {p}}$ ) with the pressure in the gas chamber within the range of 0.1–0.3 MPa. When the gas chamber is filled with 10000-ppm C2H2/N2 to 0.3 MPa, At and Ap are 47.98% and 22.73%, respectively. A good linear relationship exists between the attenuation value of photodetector (PD) output and target gas concentration with a correlation coefficient above 0.99 after 140 s. The theoretical minimum detection limit of C2H2 without the erbium-doped fiber amplifier (EDFA) is 49.28 ppm and the relative synthetic standard uncertainty is 3.35%. This article provides an idea for applying the HC-PCF-based gas sensing system.
{"title":"Performance of Hollow-Core Photonic Crystal Fiber-Based Trace C₂H₂ Detection System","authors":"Xianzong Chao;Fuping Zeng;Hongtu Cheng;Xinghai Jiang;Zujian Huang;Qiang Yao;Ju Tang","doi":"10.1109/TIM.2025.3534217","DOIUrl":"https://doi.org/10.1109/TIM.2025.3534217","url":null,"abstract":"In this article, we propose an optical sensor system utilizing the capabilities of hollow-core photonic crystal fiber (HC-PCF) to transmit excitation light and hold sample gas. The direct absorption response is examined for different modulation waveforms, pressures, frequencies, and concentrations. The experimental results show that the response signal has an obvious overshoot and spike. The overshoot and spike are more obvious under the sine modulation than the rectangular modulation. In the 1–500-Hz range, the system demonstrated optimum response characteristics at 5 Hz, corresponding to the maximum attenuation percentage. Furthermore, a positive correlation was observed between the attenuation percentage of light intensity (<inline-formula> <tex-math>${A} _{text {t}}$ </tex-math></inline-formula>) and optical power (<inline-formula> <tex-math>${A} _{text {p}}$ </tex-math></inline-formula>) with the pressure in the gas chamber within the range of 0.1–0.3 MPa. When the gas chamber is filled with 10000-ppm C2H2/N2 to 0.3 MPa, At and Ap are 47.98% and 22.73%, respectively. A good linear relationship exists between the attenuation value of photodetector (PD) output and target gas concentration with a correlation coefficient above 0.99 after 140 s. The theoretical minimum detection limit of C2H2 without the erbium-doped fiber amplifier (EDFA) is 49.28 ppm and the relative synthetic standard uncertainty is 3.35%. This article provides an idea for applying the HC-PCF-based gas sensing system.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361173","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}
This work proposes a noise figure (NF) measurement method for active antennas based on the over-the-air (OTA) method. First, the relevant concepts of NFs are reviewed. Then, the calibration of path gain is proposed and the calculation for the NF is deduced. Finally, the measurement setup and procedure are presented, and the measurements are performed. The results using the proposed method and those from traditional conduction measurement are compared, which verifies our idea. Compared to the existing methods, the proposed method has the advantage of independence on the measurement distance between the transmit antenna and the active antenna under test (AAUT) in a specified range.
{"title":"A Method for Over-the-Air Noise Figure Measurement of Active Antenna Under Specified Practical Distance","authors":"Haidong Chen;Mingyi Yuan;Guangxu Shen;Shuyou Gan;Wenquan Che;Quan Xue","doi":"10.1109/TIM.2025.3533617","DOIUrl":"https://doi.org/10.1109/TIM.2025.3533617","url":null,"abstract":"This work proposes a noise figure (NF) measurement method for active antennas based on the over-the-air (OTA) method. First, the relevant concepts of NFs are reviewed. Then, the calibration of path gain is proposed and the calculation for the NF is deduced. Finally, the measurement setup and procedure are presented, and the measurements are performed. The results using the proposed method and those from traditional conduction measurement are compared, which verifies our idea. Compared to the existing methods, the proposed method has the advantage of independence on the measurement distance between the transmit antenna and the active antenna under test (AAUT) in a specified range.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-7"},"PeriodicalIF":5.6,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361127","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 : 2025-02-06DOI: 10.1109/TIM.2025.3535575
Gen Shi;Lin Yin;Zhongwei Bian;Ziwei Chen;Yu An;Hui Hui;Jie Tian
Magnetic particle imaging (MPI) has emerged as a promising medical imaging technique known for its high sensitivity and high imaging speed, making real-time, in vivo imaging feasible. However, existing MPI systems often require multiple repetition measurements for signal denoising. Few repetitions may result in low-quality images with increased noise, whereas many repetitions compromise temporal resolution and may introduce significant motion artifacts in dynamic imaging. Therefore, to fully exploit the advantages of MPI in real-time imaging, it is crucial to reduce the repetition number while maintaining high-quality images. In this study, we introduced a novel deep-learning (DL)-based approach, the content-aware distillation network (CAD-Net), for accelerated MPI. The method reconstructs high-quality images by denoising noisy images, typically acquired with a limited number of repetitions (tens of milliseconds). CAD-Net incorporates a proposed multiscale content-aware (MCA) block to accurately model noise distribution and enhance denoising performance. In addition, we proposed an activation-mask-based distillation strategy to reduce model processing time, particularly important for real-time imaging. Evaluation on a public real-world dataset, OpenMPI, and a simulation dataset, proved that CAD-Net outperformed existing methods in denoising performance and model efficiency. Compared to traditional methods based on multiple measurements, CAD-Net increased the frames per second (FPS) metric by approximately 70 times. Experiments on in-house data demonstrated the applicability of CAD-Net in MPI denoising in in vitro and in vivo imaging. CAD-Net improved image quality in real-time denoising with only a marginal increase in time cost. The code and data will be available at: https://github.com/shigen-StoneRoot/CAD-Net.git.
{"title":"Content-Aware Distillation Network for Real-Time Magnetic Particle Imaging","authors":"Gen Shi;Lin Yin;Zhongwei Bian;Ziwei Chen;Yu An;Hui Hui;Jie Tian","doi":"10.1109/TIM.2025.3535575","DOIUrl":"https://doi.org/10.1109/TIM.2025.3535575","url":null,"abstract":"Magnetic particle imaging (MPI) has emerged as a promising medical imaging technique known for its high sensitivity and high imaging speed, making real-time, in vivo imaging feasible. However, existing MPI systems often require multiple repetition measurements for signal denoising. Few repetitions may result in low-quality images with increased noise, whereas many repetitions compromise temporal resolution and may introduce significant motion artifacts in dynamic imaging. Therefore, to fully exploit the advantages of MPI in real-time imaging, it is crucial to reduce the repetition number while maintaining high-quality images. In this study, we introduced a novel deep-learning (DL)-based approach, the content-aware distillation network (CAD-Net), for accelerated MPI. The method reconstructs high-quality images by denoising noisy images, typically acquired with a limited number of repetitions (tens of milliseconds). CAD-Net incorporates a proposed multiscale content-aware (MCA) block to accurately model noise distribution and enhance denoising performance. In addition, we proposed an activation-mask-based distillation strategy to reduce model processing time, particularly important for real-time imaging. Evaluation on a public real-world dataset, OpenMPI, and a simulation dataset, proved that CAD-Net outperformed existing methods in denoising performance and model efficiency. Compared to traditional methods based on multiple measurements, CAD-Net increased the frames per second (FPS) metric by approximately 70 times. Experiments on in-house data demonstrated the applicability of CAD-Net in MPI denoising in in vitro and in vivo imaging. CAD-Net improved image quality in real-time denoising with only a marginal increase in time cost. The code and data will be available at: <uri>https://github.com/shigen-StoneRoot/CAD-Net.git</uri>.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143388581","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 : 2025-02-06DOI: 10.1109/TIM.2025.3533628
Xindi Zhang;Xue Zhou;Yanan Zhang;Linh Viet Nguyen;Yong Zhao;Stephen C. Warren-Smith;Xuegang Li
A novel fiber optic sensor has been developed using suspended core fiber (SCF) to simultaneously measure the refractive index (RI) and temperature of liquids. The innovative design comprises an SCF segment spliced between two tapered single-mode fibers (SMFs). The SCF allows the transmission of multiple modes, thus enabling multimode interference. Importantly, the three internal holes of the SCF function as microfluidic channels, providing direct access to the evanescent field of the micrometer-scaled core. This direct interaction significantly enhances the sensor’s sensitivity by intensifying the light-sample interaction. Each pair of interfering optical modes exhibits distinct sensitivity to both temperature and RI. By performing Fourier analysis, the interference spectrum for each mode pair can be extracted. High sensitivity is achieved, with the values of 1560 and 1217 nm/RIU for RI within the range 1.3309–1.3350 and −0.25 nm/°C and −0.31 nm/°C for temperatures ranging from 20 °C to 40 °C. The resolutions of RI and temperature were $1.3 times 10^{-5}$ RIU and 0.06 °C, respectively. The transfer matrix method effectively eliminates temperature interference during RI measurement, ensuring reliable and accurate RI readings. The sensor is shown to have a detection limit as low as $6.1 times 10^{-5}$ RIU and 0.3 °C. Combining its desirable characteristics of good stability, a simple structure, and high sensitivity, this novel sensor holds significant promise for diverse applications in environmental monitoring, medical testing, and biological sensing.
{"title":"Highly Sensitive Fiber Optic Sensor for Simultaneous Refractive Index and Temperature Measurement Using Suspended Core Fiber","authors":"Xindi Zhang;Xue Zhou;Yanan Zhang;Linh Viet Nguyen;Yong Zhao;Stephen C. Warren-Smith;Xuegang Li","doi":"10.1109/TIM.2025.3533628","DOIUrl":"https://doi.org/10.1109/TIM.2025.3533628","url":null,"abstract":"A novel fiber optic sensor has been developed using suspended core fiber (SCF) to simultaneously measure the refractive index (RI) and temperature of liquids. The innovative design comprises an SCF segment spliced between two tapered single-mode fibers (SMFs). The SCF allows the transmission of multiple modes, thus enabling multimode interference. Importantly, the three internal holes of the SCF function as microfluidic channels, providing direct access to the evanescent field of the micrometer-scaled core. This direct interaction significantly enhances the sensor’s sensitivity by intensifying the light-sample interaction. Each pair of interfering optical modes exhibits distinct sensitivity to both temperature and RI. By performing Fourier analysis, the interference spectrum for each mode pair can be extracted. High sensitivity is achieved, with the values of 1560 and 1217 nm/RIU for RI within the range 1.3309–1.3350 and −0.25 nm/°C and −0.31 nm/°C for temperatures ranging from 20 °C to 40 °C. The resolutions of RI and temperature were <inline-formula> <tex-math>$1.3 times 10^{-5}$ </tex-math></inline-formula> RIU and 0.06 °C, respectively. The transfer matrix method effectively eliminates temperature interference during RI measurement, ensuring reliable and accurate RI readings. The sensor is shown to have a detection limit as low as <inline-formula> <tex-math>$6.1 times 10^{-5}$ </tex-math></inline-formula> RIU and 0.3 °C. Combining its desirable characteristics of good stability, a simple structure, and high sensitivity, this novel sensor holds significant promise for diverse applications in environmental monitoring, medical testing, and biological sensing.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-7"},"PeriodicalIF":5.6,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143369958","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}
Bearing fault diagnosis is crucial for maintaining the safety of industrial systems. With the massive data collected by the Industrial Internet-of-Things technology, deep learning (DL)-based end-to-end models have been extensively utilized in bearing fault diagnosis. However, their limited interpretability poses challenges to their reliability, hindering further advancements in the field. To address this interpretability issue, we propose a globally interpretable convolutional neural network (CNN) combining bearing semantics for bearing fault diagnosis. Specifically, the physical semantics of bearing signals are first constructed based on the fault characteristic frequency (FCF). Based on this bearing semantics, a novel bearing semantic embedding method is proposed to enhance the interpretability of convolutional layers. Moreover, a globally interpretable network (GINet) structure is crafted to ensure that the bearing semantics are visible throughout the entire network. Experimental results on two datasets demonstrate that the network’s performance remains comparable to the benchmark method while achieving global interpretability. This network also exhibits improved noise robustness, proving the effectiveness of semantic embedding. In addition, since this network is an interpretable modification of the basic CNN, it is not limited to bearing fault diagnosis. Theoretically, with the appropriate semantics, it can also be applied to other signal-based fault diagnosis tasks.
{"title":"A Globally Interpretable Convolutional Neural Network Combining Bearing Semantics for Bearing Fault Diagnosis","authors":"Zhen Wang;Guangjie Han;Li Liu;Feng Wang;Yuanyang Zhu","doi":"10.1109/TIM.2025.3538068","DOIUrl":"https://doi.org/10.1109/TIM.2025.3538068","url":null,"abstract":"Bearing fault diagnosis is crucial for maintaining the safety of industrial systems. With the massive data collected by the Industrial Internet-of-Things technology, deep learning (DL)-based end-to-end models have been extensively utilized in bearing fault diagnosis. However, their limited interpretability poses challenges to their reliability, hindering further advancements in the field. To address this interpretability issue, we propose a globally interpretable convolutional neural network (CNN) combining bearing semantics for bearing fault diagnosis. Specifically, the physical semantics of bearing signals are first constructed based on the fault characteristic frequency (FCF). Based on this bearing semantics, a novel bearing semantic embedding method is proposed to enhance the interpretability of convolutional layers. Moreover, a globally interpretable network (GINet) structure is crafted to ensure that the bearing semantics are visible throughout the entire network. Experimental results on two datasets demonstrate that the network’s performance remains comparable to the benchmark method while achieving global interpretability. This network also exhibits improved noise robustness, proving the effectiveness of semantic embedding. In addition, since this network is an interpretable modification of the basic CNN, it is not limited to bearing fault diagnosis. Theoretically, with the appropriate semantics, it can also be applied to other signal-based fault diagnosis tasks.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403926","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 : 2025-02-04DOI: 10.1109/TIM.2025.3534228
Kaiyan Lei;Zhiquan Qi
For the anomaly detection on the surface of rail transit train body (RTTB-AD), due to the scarcity of anomalies, the complexity and variability of the detection environment, and the exceptionally high identification rate required by practical application, the task is quite challenging. This article proposes a novel differential-based anomaly detection model (DSE-AD) for the surface of rail train bodies based on visual-language model. It utilizes the differences between history and current images of the same position on the same train type to achieve anomaly localization, while addressing nonanomalous changes interference caused by the environment. Specifically, we first propose the normal-abnormal dual-state contrast prompt suitable for rail trains, and fine-grained align the image features with the prompt features from the pretrained encoder to obtain the task-specific dual-state feature representation. Next, we propose the dual-state difference enhancement (DSDE) module, which utilizes a learnable difference attention matrix to enhance the anomaly-specific dual-state information, allowing the model to focus on the anomaly semantics. Finally, a anomaly highlight module (AHM) is designed in the inference process to reduce nonanomalous predictions by improving the discrimination of abnormal features. Experiments show that DSE-AD is able to adapt to the complex and variable detection environment, and outperforms other methods in both same-domain and cross-domain detection, especially for unknown anomalies. And it shows robustness in dealing with the interference of changes between the history and current images, as well as faster convergence and independence of the pretrained model scale.
{"title":"A Dual-State-Based Surface Anomaly Detection Model for Rail Transit Trains Using Vision-Language Model","authors":"Kaiyan Lei;Zhiquan Qi","doi":"10.1109/TIM.2025.3534228","DOIUrl":"https://doi.org/10.1109/TIM.2025.3534228","url":null,"abstract":"For the anomaly detection on the surface of rail transit train body (RTTB-AD), due to the scarcity of anomalies, the complexity and variability of the detection environment, and the exceptionally high identification rate required by practical application, the task is quite challenging. This article proposes a novel differential-based anomaly detection model (DSE-AD) for the surface of rail train bodies based on visual-language model. It utilizes the differences between history and current images of the same position on the same train type to achieve anomaly localization, while addressing nonanomalous changes interference caused by the environment. Specifically, we first propose the normal-abnormal dual-state contrast prompt suitable for rail trains, and fine-grained align the image features with the prompt features from the pretrained encoder to obtain the task-specific dual-state feature representation. Next, we propose the dual-state difference enhancement (DSDE) module, which utilizes a learnable difference attention matrix to enhance the anomaly-specific dual-state information, allowing the model to focus on the anomaly semantics. Finally, a anomaly highlight module (AHM) is designed in the inference process to reduce nonanomalous predictions by improving the discrimination of abnormal features. Experiments show that DSE-AD is able to adapt to the complex and variable detection environment, and outperforms other methods in both same-domain and cross-domain detection, especially for unknown anomalies. And it shows robustness in dealing with the interference of changes between the history and current images, as well as faster convergence and independence of the pretrained model scale.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143489066","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 : 2025-02-03DOI: 10.1109/TIM.2025.3538087
Shubhanshu Sharma;Boby George;Chenglin Lyu;Philip von Platen;Markus Lüken;Marian Walter;L. Cornelius Bollheimer;Steffen Leonhardt
The type of walking pathway influences gait. Thus, a wearable gait sensing technique is important for continuous gait analysis. However, most of the wearable sensing technologies employed in gait analysis solely provide data on gait parameters and do not have mechanisms to sense and account for the type of pathways. In this article, a novel technique is developed to simultaneously identify some of the spatiotemporal gait parameters and the type of pathway on which the subject is walking. This is achieved by measuring the electrical impedance of the floor between the shoes employing a measurement system reported recently. This article shows that gait parameters can be derived using the impedance values measured between the shoes. These impedance values change as the legs move, primarily due to changes in capacitance between the shoe and the pathway. A suitable algorithm is developed and tested to estimate the gait parameters and walking speed from the developed prototype, and this is compared with the parameters obtained from reference force plate-based sensing. The testing is done on seven human subjects. The average root-mean-square error (RMSE) values for different gait parameters were found to be 0.02 s, 1.4 cm, 2.6 cm, 0.07 s, 0.8 steps/min, 2.24%, and 2.24%, for stride time (STT), stride length (STL), step length (SL), step time (ST), cadence (CD), stance phase (SP), swing phase (SWP), respectively, and a worst-case error of ±5% in walking speed is observed. Further, the human subjects walked on different pedestrian pathways. Different features were extracted from the impedance waveform, which helped in successfully classifying all the six types of pathways we tested.
{"title":"Enhancing Gait Analysis and Pathway Classification Through Ground Impedance-Based Shoes: An Innovative Approach","authors":"Shubhanshu Sharma;Boby George;Chenglin Lyu;Philip von Platen;Markus Lüken;Marian Walter;L. Cornelius Bollheimer;Steffen Leonhardt","doi":"10.1109/TIM.2025.3538087","DOIUrl":"https://doi.org/10.1109/TIM.2025.3538087","url":null,"abstract":"The type of walking pathway influences gait. Thus, a wearable gait sensing technique is important for continuous gait analysis. However, most of the wearable sensing technologies employed in gait analysis solely provide data on gait parameters and do not have mechanisms to sense and account for the type of pathways. In this article, a novel technique is developed to simultaneously identify some of the spatiotemporal gait parameters and the type of pathway on which the subject is walking. This is achieved by measuring the electrical impedance of the floor between the shoes employing a measurement system reported recently. This article shows that gait parameters can be derived using the impedance values measured between the shoes. These impedance values change as the legs move, primarily due to changes in capacitance between the shoe and the pathway. A suitable algorithm is developed and tested to estimate the gait parameters and walking speed from the developed prototype, and this is compared with the parameters obtained from reference force plate-based sensing. The testing is done on seven human subjects. The average root-mean-square error (RMSE) values for different gait parameters were found to be 0.02 s, 1.4 cm, 2.6 cm, 0.07 s, 0.8 steps/min, 2.24%, and 2.24%, for stride time (STT), stride length (STL), step length (SL), step time (ST), cadence (CD), stance phase (SP), swing phase (SWP), respectively, and a worst-case error of ±5% in walking speed is observed. Further, the human subjects walked on different pedestrian pathways. Different features were extracted from the impedance waveform, which helped in successfully classifying all the six types of pathways we tested.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143438533","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}
Lithium-ion batteries’ state of health (SOH) predictions are essential for the safe use of batteries. SOH prediction methods based on the empirical mode decomposition (EMD) framework can effectively suppress the negative impact of capacity regeneration (CR), while the problem of data distribution discrepancy is not considered. To tackle this problem, a prediction method with models transfer and error compensation under the EMD framework is proposed. First, the dynamic time warping (DTW) algorithm is used to select a reference lithium-ion battery from the dataset. Second, the intrinsic mode functions (IMFs) are acquired from the capacity data decomposed by improved complete ensemble EMD with adaptive noise (ICEEMDAN), and the IMFs are reconstructed based on the Hurst exponent values. Then, the proposed temporal dual-channel networks (TDCNs) are pre-trained by the reconstructed IMFs of the reference battery and fine-tuned by the reconstructed IMFs of the target battery. An error compensation method based on secondary decomposition is proposed for further improvement of the prediction accuracy. The error sequence is decomposed by variational mode decomposition (VMD) and predicted by TDCNs. The final SOH prediction results are acquired based on the preliminary capacity predictions and the error predictions. The effectiveness of the proposed method is validated on NASA Prognostics Center of Excellence (PCoE) and Center for Advanced Life Cycle Engineering (CALCE) datasets. The results show that, after introducing the models transfer method, the root-mean-square errors (RMSEs) of the prediction results for the two datasets have been reduced by 90.0% and 95.6% on average, respectively. After error compensation, the RMSEs have been reduced by 62.9% and 66.6% on average, respectively.
{"title":"A Lithium-Ion Battery SOH Prediction Method: Temporal Dual-Channel Networks Transfer and Error Compensation Under the EMD Framework","authors":"Xiongbo Wan;Fan Mao;Xingyu Zhao;Chuan-Ke Zhang;Wenkai Hu;Min Wu","doi":"10.1109/TIM.2025.3538072","DOIUrl":"https://doi.org/10.1109/TIM.2025.3538072","url":null,"abstract":"Lithium-ion batteries’ state of health (SOH) predictions are essential for the safe use of batteries. SOH prediction methods based on the empirical mode decomposition (EMD) framework can effectively suppress the negative impact of capacity regeneration (CR), while the problem of data distribution discrepancy is not considered. To tackle this problem, a prediction method with models transfer and error compensation under the EMD framework is proposed. First, the dynamic time warping (DTW) algorithm is used to select a reference lithium-ion battery from the dataset. Second, the intrinsic mode functions (IMFs) are acquired from the capacity data decomposed by improved complete ensemble EMD with adaptive noise (ICEEMDAN), and the IMFs are reconstructed based on the Hurst exponent values. Then, the proposed temporal dual-channel networks (TDCNs) are pre-trained by the reconstructed IMFs of the reference battery and fine-tuned by the reconstructed IMFs of the target battery. An error compensation method based on secondary decomposition is proposed for further improvement of the prediction accuracy. The error sequence is decomposed by variational mode decomposition (VMD) and predicted by TDCNs. The final SOH prediction results are acquired based on the preliminary capacity predictions and the error predictions. The effectiveness of the proposed method is validated on NASA Prognostics Center of Excellence (PCoE) and Center for Advanced Life Cycle Engineering (CALCE) datasets. The results show that, after introducing the models transfer method, the root-mean-square errors (RMSEs) of the prediction results for the two datasets have been reduced by 90.0% and 95.6% on average, respectively. After error compensation, the RMSEs have been reduced by 62.9% and 66.6% on average, respectively.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403903","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 : 2025-02-03DOI: 10.1109/TIM.2025.3538076
Jie Wang;Lu Lu;Guangya Zhu;Kai-Li Yin;Kai Zhou;Badong Chen
Utilizing the unbiasedness criterion, this article proposes a bias-compensated normalized Euclidean direction search (BC-NEDS) algorithm with noisy inputs, which can effectively mitigate the impact of the bias by estimating the statistical properties of inputs. Moreover, the theoretical analysis of the BC-NEDS algorithm is conducted in a transient regime. Simulations verify the validity of the theoretical analysis and showcase the improved performance of the BC-NEDS algorithm. Partial discharge (PD) location techniques can be utilized to effectively monitor the condition of the electrical apparatus. Considering the performance of the conventional location algorithms based on the time difference of arrival (TDOA) method may notably degrade with noisy inputs. Hitherto, scarce literature concentrates on the PD location problem by leveraging adaptive filtering techniques. Such problem is essentially characterized by a noisy input model. This work aims to propose a new one-step algorithm to simultaneously denoise and achieve improved location accuracy with noisy input. The BC-NEDS algorithm is employed for solving the PD location problem. The BC-NEDS algorithm demonstrates the effectiveness in mitigating the bias arising from noisy inputs in both the direct and reflected PD signals and estimating the time difference to locate the PD signal of cable systems. Simulations and experimental studies exhibit that the BC-NEDS algorithm verifies the effectiveness and achieves enhanced location accuracy for cable systems.
{"title":"Unbiased Euclidean Direction Search Algorithm for Partial Discharge Location in Cable Systems","authors":"Jie Wang;Lu Lu;Guangya Zhu;Kai-Li Yin;Kai Zhou;Badong Chen","doi":"10.1109/TIM.2025.3538076","DOIUrl":"https://doi.org/10.1109/TIM.2025.3538076","url":null,"abstract":"Utilizing the unbiasedness criterion, this article proposes a bias-compensated normalized Euclidean direction search (BC-NEDS) algorithm with noisy inputs, which can effectively mitigate the impact of the bias by estimating the statistical properties of inputs. Moreover, the theoretical analysis of the BC-NEDS algorithm is conducted in a transient regime. Simulations verify the validity of the theoretical analysis and showcase the improved performance of the BC-NEDS algorithm. Partial discharge (PD) location techniques can be utilized to effectively monitor the condition of the electrical apparatus. Considering the performance of the conventional location algorithms based on the time difference of arrival (TDOA) method may notably degrade with noisy inputs. Hitherto, scarce literature concentrates on the PD location problem by leveraging adaptive filtering techniques. Such problem is essentially characterized by a noisy input model. This work aims to propose a new one-step algorithm to simultaneously denoise and achieve improved location accuracy with noisy input. The BC-NEDS algorithm is employed for solving the PD location problem. The BC-NEDS algorithm demonstrates the effectiveness in mitigating the bias arising from noisy inputs in both the direct and reflected PD signals and estimating the time difference to locate the PD signal of cable systems. Simulations and experimental studies exhibit that the BC-NEDS algorithm verifies the effectiveness and achieves enhanced location accuracy for cable systems.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403832","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 : 2025-02-03DOI: 10.1109/TIM.2025.3538063
Pijush Kanti Dhara;Zakir Hussain Rather;Chitaranjan Phurailatpam
Displacement of conventional generations with the rapid adoption of renewable energy (RE) sources has led to diminishing system inertia and active power reserves. Consequently, power systems with high shares of RE sources have become more susceptible to frequency stability issues, often experiencing higher rates of change of frequency (RoCoF) and frequency deviations. Accurate estimation of essential system services required to maintain a stable and economic operation has become crucial. This article proposes a new method for the accurate estimation of minimum synchronous inertia and frequency containment reserve (FCR) response to ensure compliance with system-specified RoCoF and frequency deviation limits. First, the minimum synchronous inertia requirement for a specified RoCoF limit is calculated, which also considers the instantaneous response from demand, Type-I and Type-II wind generators, which are generally difficult to monitor. The overall FCR requirement is then estimated to maintain the frequency deviation within the predefined limits. Dynamic FCR response includes fast frequency response (FFR) from inverter-based resources (IBRs) and responses from load and governors from conventional generations. Finally, the share of FFR contribution coming from IBRs, representing the system service requirement, is segregated from the overall FCR and estimated accurately. With the proposed method, system operators can be best informed of the exact minimum inertia (MI) and FFR requirement to ensure frequency stability. The proposed methodology is tested and validated in a modified IEEE 39-bus system and an actual grid model of the Gujarat State in India.
{"title":"Estimation of Minimum Inertia and Fast Frequency Support for Renewable Energy Dominated Power Systems","authors":"Pijush Kanti Dhara;Zakir Hussain Rather;Chitaranjan Phurailatpam","doi":"10.1109/TIM.2025.3538063","DOIUrl":"https://doi.org/10.1109/TIM.2025.3538063","url":null,"abstract":"Displacement of conventional generations with the rapid adoption of renewable energy (RE) sources has led to diminishing system inertia and active power reserves. Consequently, power systems with high shares of RE sources have become more susceptible to frequency stability issues, often experiencing higher rates of change of frequency (RoCoF) and frequency deviations. Accurate estimation of essential system services required to maintain a stable and economic operation has become crucial. This article proposes a new method for the accurate estimation of minimum synchronous inertia and frequency containment reserve (FCR) response to ensure compliance with system-specified RoCoF and frequency deviation limits. First, the minimum synchronous inertia requirement for a specified RoCoF limit is calculated, which also considers the instantaneous response from demand, Type-I and Type-II wind generators, which are generally difficult to monitor. The overall FCR requirement is then estimated to maintain the frequency deviation within the predefined limits. Dynamic FCR response includes fast frequency response (FFR) from inverter-based resources (IBRs) and responses from load and governors from conventional generations. Finally, the share of FFR contribution coming from IBRs, representing the system service requirement, is segregated from the overall FCR and estimated accurately. With the proposed method, system operators can be best informed of the exact minimum inertia (MI) and FFR requirement to ensure frequency stability. The proposed methodology is tested and validated in a modified IEEE 39-bus system and an actual grid model of the Gujarat State in India.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143465559","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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