Pub Date : 2024-05-22DOI: 10.1088/1361-6501/ad4ab7
Ci Song, Zhibing Liu, Xibin Wang, Tianyang Qiu, Zhiqiang Liang, Wenhua Shen, Yuhang Gao, Senjie Ma
In robotic side milling, frequent chatter extremely restricts the acquisition of high surface quality due to weak stiffness, and cutting parameters optimization guided by stability boundary is regarded as an effective solution to solve the chatter problem. In this research, the influence mechanisms of stability were analyzed by evaluating the structural static stiffness and dynamic parameters, and the main factor was characterized as regenerative chatter by means of stability measurements and the theoretical prediction model. The distance-driven multi-posture frequency response function (FRF) prediction model was improved in terms of the dominant modal. Grey correlation analysis was carried out to investigate the influence law of robotic joints to modal parameters, and the difference between far-distance posture and near-distance posture was re-characterized by cross-validation of FRF measurements. Finally, the third-order Hermite–Newton approximation was employed to solve the dynamic model by considering process damping effect, and the results showed the prediction accuracy of the constructed stability boundary was over 85%.
{"title":"Research on the stability prediction for multi-posture robotic side milling based on FRF measurements","authors":"Ci Song, Zhibing Liu, Xibin Wang, Tianyang Qiu, Zhiqiang Liang, Wenhua Shen, Yuhang Gao, Senjie Ma","doi":"10.1088/1361-6501/ad4ab7","DOIUrl":"https://doi.org/10.1088/1361-6501/ad4ab7","url":null,"abstract":"\u0000 In robotic side milling, frequent chatter extremely restricts the acquisition of high surface quality due to weak stiffness, and cutting parameters optimization guided by stability boundary is regarded as an effective solution to solve the chatter problem. In this research, the influence mechanisms of stability were analyzed by evaluating the structural static stiffness and dynamic parameters, and the main factor was characterized as regenerative chatter by means of stability measurements and the theoretical prediction model. The distance-driven multi-posture frequency response function (FRF) prediction model was improved in terms of the dominant modal. Grey correlation analysis was carried out to investigate the influence law of robotic joints to modal parameters, and the difference between far-distance posture and near-distance posture was re-characterized by cross-validation of FRF measurements. Finally, the third-order Hermite–Newton approximation was employed to solve the dynamic model by considering process damping effect, and the results showed the prediction accuracy of the constructed stability boundary was over 85%.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"56 45","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-22DOI: 10.1088/1361-6501/ad4f04
Juracy Leandro dos Santos Júnior, Ian Ribeiro Andrade, Lucas Henrique Pereira Silva, Luis Abegao
This study introduces the design, construction, and evaluation of an affordable optical power meter prototype, AYR (Affordable Yet Reliable) version 1.0, which operates effectively within the 400-800 nm range, using a silicon photodiode. Aimed at bridging the gap in accessibility to precise and reliable photonics instrumentation, especially in resource-constrained settings, AYR 1.0 leverages advancements in photodiode technology, additive manufacturing, and do-it-yourself electronics. The device incorporates a custom-built electronic circuit that facilitates accurate optical power measurement by converting light into electrical current. Through rigorous testing against a reliable commercial optical power meter, AYR 1.0 demonstrated exceptional accuracy and reliability. Sensitivity values ranged from ~13 µA/mW at 405 nm to ~796 µA/mW at 805 nm. The operational power range spanned from 0.003 mW to 242.0 mW, with linearity (R²) values consistently above 0.9981, indicating high fidelity in measurement. Repeatability percentages varied between 99.4% and 99.9%, and response times ranged up to 55 µs, showcasing the prototype's rapid and reliable response to changes in optical power. The key components include a low-cost silicon photodiode (2DU10), a differential trans-impedance amplifier circuit for signal processing, and a 3D-printed housing for the sensor head and console, contributing to its cost-effectiveness and robustness. The prototype's total cost was 116 US dollars, highlighting its affordability and potential for widespread adoption.
{"title":"Design and Construction of an Affordable Optical Power Meter: Micro- to Milli-Watt in the 400-800 nm Range","authors":"Juracy Leandro dos Santos Júnior, Ian Ribeiro Andrade, Lucas Henrique Pereira Silva, Luis Abegao","doi":"10.1088/1361-6501/ad4f04","DOIUrl":"https://doi.org/10.1088/1361-6501/ad4f04","url":null,"abstract":"\u0000 This study introduces the design, construction, and evaluation of an affordable optical power meter prototype, AYR (Affordable Yet Reliable) version 1.0, which operates effectively within the 400-800 nm range, using a silicon photodiode. Aimed at bridging the gap in accessibility to precise and reliable photonics instrumentation, especially in resource-constrained settings, AYR 1.0 leverages advancements in photodiode technology, additive manufacturing, and do-it-yourself electronics. The device incorporates a custom-built electronic circuit that facilitates accurate optical power measurement by converting light into electrical current. Through rigorous testing against a reliable commercial optical power meter, AYR 1.0 demonstrated exceptional accuracy and reliability. Sensitivity values ranged from ~13 µA/mW at 405 nm to ~796 µA/mW at 805 nm. The operational power range spanned from 0.003 mW to 242.0 mW, with linearity (R²) values consistently above 0.9981, indicating high fidelity in measurement. Repeatability percentages varied between 99.4% and 99.9%, and response times ranged up to 55 µs, showcasing the prototype's rapid and reliable response to changes in optical power. The key components include a low-cost silicon photodiode (2DU10), a differential trans-impedance amplifier circuit for signal processing, and a 3D-printed housing for the sensor head and console, contributing to its cost-effectiveness and robustness. The prototype's total cost was 116 US dollars, highlighting its affordability and potential for widespread adoption.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"74 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-22DOI: 10.1088/1361-6501/ad4eff
Junning Li, Wenguang Luo, Mengsha Bai
Rolling bearings are critical components that are prone to faults in the operation of rotating equipment. Therefore, it is of utmost importance to accurately diagnose the state of rolling bearings. This review comprehensively discusses classical algorithms for fault diagnosis of rolling bearings based on vibration signal, focusing on three key aspects: data preprocessing, fault feature extraction, and fault feature identification. The main principles, key features, application difficulties, and suitable occasions for various algorithms are thoroughly examined. Additionally, different fault diagnosis methods are reviewed and compared using the Case Western Reserve University (CWRU) bearing dataset. Based on the current research status in bearing fault diagnosis, future development directions are also anticipated. It is expected that this review will serve as a valuable reference for researchers aiming to enhance their understanding and improve the technology of rolling bearing fault diagnosis.
{"title":"Review of research on signal decomposition and fault diagnosis of rolling bearing based on vibration signal","authors":"Junning Li, Wenguang Luo, Mengsha Bai","doi":"10.1088/1361-6501/ad4eff","DOIUrl":"https://doi.org/10.1088/1361-6501/ad4eff","url":null,"abstract":"\u0000 Rolling bearings are critical components that are prone to faults in the operation of rotating equipment. Therefore, it is of utmost importance to accurately diagnose the state of rolling bearings. This review comprehensively discusses classical algorithms for fault diagnosis of rolling bearings based on vibration signal, focusing on three key aspects: data preprocessing, fault feature extraction, and fault feature identification. The main principles, key features, application difficulties, and suitable occasions for various algorithms are thoroughly examined. Additionally, different fault diagnosis methods are reviewed and compared using the Case Western Reserve University (CWRU) bearing dataset. Based on the current research status in bearing fault diagnosis, future development directions are also anticipated. It is expected that this review will serve as a valuable reference for researchers aiming to enhance their understanding and improve the technology of rolling bearing fault diagnosis.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"57 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141112629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-22DOI: 10.1088/1361-6501/ad4f01
Bao Zhu, Chunmeng He
The conventional deep learning-based bearing fault diagnosis method tend to utilize denoising modules to improve the fault diagnosis performance in noisy scenes. However, the addition of denoising modules will increase expensive computational costs, leading to a delayed acquisition of fault diagnosis results. This work proposed a lightweight batch normalization-free residual network without any denoising modules for bearing fault diagnosis which properly rescaled the weights in a standard initialization instead of batch normalization to avoid the exploding gradient problem and vanishing gradient problem at the beginning of training for deep neural networks. Therefore, it prevents the undesirable properties caused by batch normalization. Compared with other methods, the fault diagnosis performance of the proposed method can maintain a high level with different input sizes and batch sizes. Especially in noisy scenes, the testing accuracy of fault diagnosis on different bearing datasets can be improved by 13.54% and 7.74% using fewer parameters and FLOPs on different bearing datasets.
{"title":"A stable and robust fault diagnosis method for bearing using lightweight batch normalization-free residual network","authors":"Bao Zhu, Chunmeng He","doi":"10.1088/1361-6501/ad4f01","DOIUrl":"https://doi.org/10.1088/1361-6501/ad4f01","url":null,"abstract":"\u0000 The conventional deep learning-based bearing fault diagnosis method tend to utilize denoising modules to improve the fault diagnosis performance in noisy scenes. However, the addition of denoising modules will increase expensive computational costs, leading to a delayed acquisition of fault diagnosis results. This work proposed a lightweight batch normalization-free residual network without any denoising modules for bearing fault diagnosis which properly rescaled the weights in a standard initialization instead of batch normalization to avoid the exploding gradient problem and vanishing gradient problem at the beginning of training for deep neural networks. Therefore, it prevents the undesirable properties caused by batch normalization. Compared with other methods, the fault diagnosis performance of the proposed method can maintain a high level with different input sizes and batch sizes. Especially in noisy scenes, the testing accuracy of fault diagnosis on different bearing datasets can be improved by 13.54% and 7.74% using fewer parameters and FLOPs on different bearing datasets.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"59 15","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141109011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The cumulative overload of conductor tension under severe weather conditions is an important cause of accelerated fatigue fracture of transmission lines. Traditional tension measurement methods require the replacement of ball head hanging rings, which poses safety risks. In this paper, a method for monitoring conductor tension based on acceleration data under operating conditions is proposed. Firstly, a modal order extraction based method for identifying the modal frequencies of conductor operation is proposed, and then the time-varying tension of the conductor is estimated based on the instantaneous modal frequencies. Since this method directly installs sensors on the conductor, there is a certain error in the obtained intrinsic characteristic data of the conductor. Therefore, a modal correction method is used to remove the influence of the sensors. The accuracy of modal identification, modal correction, and tension identification methods is verified through finite element models. Based on the above methods, a monitoring system for conductor tension status is designed, and the feasibility of this system is verified through experiments. Finally, the vibration data obtained from the field engineering pilot test is successfully used for conductor tension analysis. The results show that the proposed method can effectively identify time-varying tension and provide a new approach for monitoring the status of transmission line conductors.
{"title":"Research on online monitoring method for time-varying tension in transmission lines based on operational modal response","authors":"Zhicheng Liu, Long Zhao, Guanru Wen, Jingyao Wang, Jiameng Wang, Xinbo Huang","doi":"10.1088/1361-6501/ad4bff","DOIUrl":"https://doi.org/10.1088/1361-6501/ad4bff","url":null,"abstract":"\u0000 The cumulative overload of conductor tension under severe weather conditions is an important cause of accelerated fatigue fracture of transmission lines. Traditional tension measurement methods require the replacement of ball head hanging rings, which poses safety risks. In this paper, a method for monitoring conductor tension based on acceleration data under operating conditions is proposed. Firstly, a modal order extraction based method for identifying the modal frequencies of conductor operation is proposed, and then the time-varying tension of the conductor is estimated based on the instantaneous modal frequencies. Since this method directly installs sensors on the conductor, there is a certain error in the obtained intrinsic characteristic data of the conductor. Therefore, a modal correction method is used to remove the influence of the sensors. The accuracy of modal identification, modal correction, and tension identification methods is verified through finite element models. Based on the above methods, a monitoring system for conductor tension status is designed, and the feasibility of this system is verified through experiments. Finally, the vibration data obtained from the field engineering pilot test is successfully used for conductor tension analysis. The results show that the proposed method can effectively identify time-varying tension and provide a new approach for monitoring the status of transmission line conductors.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"50 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141108662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-22DOI: 10.1088/1361-6501/ad4f02
Kai Zhang, Xiaolin Meng, Qing Wang
Camera relocalization plays a vital role in the realms of machine perception, robotics, and augmented reality. Direct learning methods based on structures can have a learning-based approach that can learn scene coordinates and use them for camera position estimation. However, the two-stage learning of scene coordinate regression and camera position estimation can result in some of the scene coordinate regression knowledge being lost throughout the learning process of the final pose estimation system, thereby reducing the accuracy of the pose estimation. This paper introduces an innovative end-to-end learning framework tailored for visual camera relocalization by employing both RGB and RGB-D images. Distinguished by its integration of scene coordinate regression with pose estimation into a concurrent inner and outer loop during a singular training phase, this framework notably enhances pose estimation accuracy. Engineered for flexibility, it accommodates training with or without depth cues and necessitates merely a single RGB image during testing. Empirical evaluation substantiates the proposed method's state-of-the-art precision, attaining an average pose accuracy of 0.019m and 0.74º on the indoor 7Scenes dataset, together with 0.162m and 0.30º on the outdoor Cambridge Landmarks dataset.
{"title":"An End-to-end Learning Framework for Visual Camera Relocalization Using RGB and RGB-D Images","authors":"Kai Zhang, Xiaolin Meng, Qing Wang","doi":"10.1088/1361-6501/ad4f02","DOIUrl":"https://doi.org/10.1088/1361-6501/ad4f02","url":null,"abstract":"\u0000 Camera relocalization plays a vital role in the realms of machine perception, robotics, and augmented reality. Direct learning methods based on structures can have a learning-based approach that can learn scene coordinates and use them for camera position estimation. However, the two-stage learning of scene coordinate regression and camera position estimation can result in some of the scene coordinate regression knowledge being lost throughout the learning process of the final pose estimation system, thereby reducing the accuracy of the pose estimation. This paper introduces an innovative end-to-end learning framework tailored for visual camera relocalization by employing both RGB and RGB-D images. Distinguished by its integration of scene coordinate regression with pose estimation into a concurrent inner and outer loop during a singular training phase, this framework notably enhances pose estimation accuracy. Engineered for flexibility, it accommodates training with or without depth cues and necessitates merely a single RGB image during testing. Empirical evaluation substantiates the proposed method's state-of-the-art precision, attaining an average pose accuracy of 0.019m and 0.74º on the indoor 7Scenes dataset, together with 0.162m and 0.30º on the outdoor Cambridge Landmarks dataset.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"90 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141111445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-22DOI: 10.1088/1361-6501/ad4c01
Changdong Wu, Xu He, Yanliang Wu
Catenary components are an important part of electrified railways. Especially for catenary support devices, there are various types of components with significant differences in scale. According to statistical data, there is a high risk of failure for the catenary support device components during the operation of the catenary system. Therefore, in order to ensure the safe operation of the railways, it is critical to accurately locate and recognize the components in the catenary images. In this paper, we propose an improved method based on faster region-based convolutional neural networks (Faster R-CNN) framework to realize the detection and extraction of the components on the catenary support devices. Firstly, the anchor box parameters are reset using the K-means clustering method, which greatly improves the localization precision of the predicted box. Secondly, scaled exponential linear units activation function is introduced to improve the algorithm performance. Moreover, ResNet-34, the backbone of Faster R-CNN, is optimized. We design a transition structure for multi-scale filter combination convolution to avoid missing feature information and eliminate some redundant convolution structures. This modification substantially enhances the capability of the model to recognize a wide variety of component types. Finally, we conduct some control experiments comparing with single shot multibox detector and you only look once (YOLO) series (YOLOv3, YOLOv5 and YOLOv7) models. They are faster but less accurate, especially for small objects. The results show that the proposed method has better detection performance, achieving a mean average precision of 96.50% and running at 17.79 frames per second. In addition, our model has the highest average recall of 69.27%, which is 2.66% higher than the original model.
{"title":"An object detection method for catenary component images based on improved Faster R-CNN","authors":"Changdong Wu, Xu He, Yanliang Wu","doi":"10.1088/1361-6501/ad4c01","DOIUrl":"https://doi.org/10.1088/1361-6501/ad4c01","url":null,"abstract":"\u0000 Catenary components are an important part of electrified railways. Especially for catenary support devices, there are various types of components with significant differences in scale. According to statistical data, there is a high risk of failure for the catenary support device components during the operation of the catenary system. Therefore, in order to ensure the safe operation of the railways, it is critical to accurately locate and recognize the components in the catenary images. In this paper, we propose an improved method based on faster region-based convolutional neural networks (Faster R-CNN) framework to realize the detection and extraction of the components on the catenary support devices. Firstly, the anchor box parameters are reset using the K-means clustering method, which greatly improves the localization precision of the predicted box. Secondly, scaled exponential linear units activation function is introduced to improve the algorithm performance. Moreover, ResNet-34, the backbone of Faster R-CNN, is optimized. We design a transition structure for multi-scale filter combination convolution to avoid missing feature information and eliminate some redundant convolution structures. This modification substantially enhances the capability of the model to recognize a wide variety of component types. Finally, we conduct some control experiments comparing with single shot multibox detector and you only look once (YOLO) series (YOLOv3, YOLOv5 and YOLOv7) models. They are faster but less accurate, especially for small objects. The results show that the proposed method has better detection performance, achieving a mean average precision of 96.50% and running at 17.79 frames per second. In addition, our model has the highest average recall of 69.27%, which is 2.66% higher than the original model.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"14 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141110025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Distributed long gauge strain sensing technology has solved the problem of difficult identification of local damage in traditional "point" monitoring, and has received extensive attention in the field of structural damage identification. Owing to the inevitable presence of measurement noise and environmental factors in the macro strain response measurement, a single damage index has also underlined some drawbacks generally arising when multiple damages occur, or errors affect the identified dynamic properties of the systems. To address these challenges, this paper proposes a data fusion method based on the Dempster-Shafer evidence theory, relying on distributed strain sensing technology. The identification results of the modal macro strain-based index and quasi-static macro strain energy-based damage index are fused to make a comprehensive decision on structural damage location. Damage identification studies are conducted on different types of structures under impact loads and random wind loads to verify the effectiveness and accuracy of the proposed data fusion method in the case of single and multiple damage conditions. The results show that the proposed data fusion method can accurately identify the damage location and effectively reduce misjudgement on undamaged locations; it has potential application value in practical structural health monitoring.
{"title":"A data fusion-based approach for structural damage detection with distributed long-gauge strain measurements","authors":"Zhenwei Zhou, Kaiqing Ding, Wangwang Fang, Wang Shen, Yanchao Shao, Bitao Wu","doi":"10.1088/1361-6501/ad4f03","DOIUrl":"https://doi.org/10.1088/1361-6501/ad4f03","url":null,"abstract":"\u0000 Distributed long gauge strain sensing technology has solved the problem of difficult identification of local damage in traditional \"point\" monitoring, and has received extensive attention in the field of structural damage identification. Owing to the inevitable presence of measurement noise and environmental factors in the macro strain response measurement, a single damage index has also underlined some drawbacks generally arising when multiple damages occur, or errors affect the identified dynamic properties of the systems. To address these challenges, this paper proposes a data fusion method based on the Dempster-Shafer evidence theory, relying on distributed strain sensing technology. The identification results of the modal macro strain-based index and quasi-static macro strain energy-based damage index are fused to make a comprehensive decision on structural damage location. Damage identification studies are conducted on different types of structures under impact loads and random wind loads to verify the effectiveness and accuracy of the proposed data fusion method in the case of single and multiple damage conditions. The results show that the proposed data fusion method can accurately identify the damage location and effectively reduce misjudgement on undamaged locations; it has potential application value in practical structural health monitoring.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"54 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.1088/1361-6501/ad4e54
Xinyi Yuan, Yiyu Wang, Weibin Li, Mingxi Deng
Lithium-ion batteries content complex internal components, such as porous media and electrolytes, which result in strong scattering and high attenuation of ultrasonic waves in these batteries. The low attenuative feature of the quasi-static components (QSC) of ultrasonic waves offers great potential for nondestructive assessment of highly attenuating and porous materials. This paper presents an innovative approach for estimating the state-of-charge (SOC) of lithium-ion batteries using QSC of ultrasonic waves. Experimental results demonstrate a clear and repeatable linear relationship between the amplitudes of the generated QSC and the SOC of lithium-ion batteries. In addition, the relationships between different SOCs of the battery and the conventional linear ultrasonic parameters, second harmonic generation (SHG), and the QSC were compared to verify the improved sensitivity of the proposed approach. Notably, compared to linear ultrasonic features and the SHG, the generated QSC shows much higher sensitivity to the variations of SOC. We employ the phase-reversal method to further enhance the signal-to-noise ratio (SNR) of measured QSC signals. The experimental results demonstrate that the proposed method exhibits a heightened sensitivity to changes in the SOC of batteries, resulting in significantly enhanced detection accuracy and resolution. This method effectively addresses the deficiencies observed in the current detection methods such as limited accuracy and sluggish response times. This method provides a new solution to overcome this challenge. Meanwhile, it also confirms that nonlinear ultrasound promises an alternative method for SOC assessment, providing a foundation for efficient and safe battery management practices.
{"title":"A novel approach for state-of-charge estimation of lithium-ion batteries by quasi-static component generation of ultrasonic waves","authors":"Xinyi Yuan, Yiyu Wang, Weibin Li, Mingxi Deng","doi":"10.1088/1361-6501/ad4e54","DOIUrl":"https://doi.org/10.1088/1361-6501/ad4e54","url":null,"abstract":"\u0000 Lithium-ion batteries content complex internal components, such as porous media and electrolytes, which result in strong scattering and high attenuation of ultrasonic waves in these batteries. The low attenuative feature of the quasi-static components (QSC) of ultrasonic waves offers great potential for nondestructive assessment of highly attenuating and porous materials. This paper presents an innovative approach for estimating the state-of-charge (SOC) of lithium-ion batteries using QSC of ultrasonic waves. Experimental results demonstrate a clear and repeatable linear relationship between the amplitudes of the generated QSC and the SOC of lithium-ion batteries. In addition, the relationships between different SOCs of the battery and the conventional linear ultrasonic parameters, second harmonic generation (SHG), and the QSC were compared to verify the improved sensitivity of the proposed approach. Notably, compared to linear ultrasonic features and the SHG, the generated QSC shows much higher sensitivity to the variations of SOC. We employ the phase-reversal method to further enhance the signal-to-noise ratio (SNR) of measured QSC signals. The experimental results demonstrate that the proposed method exhibits a heightened sensitivity to changes in the SOC of batteries, resulting in significantly enhanced detection accuracy and resolution. This method effectively addresses the deficiencies observed in the current detection methods such as limited accuracy and sluggish response times. This method provides a new solution to overcome this challenge. Meanwhile, it also confirms that nonlinear ultrasound promises an alternative method for SOC assessment, providing a foundation for efficient and safe battery management practices.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"37 17","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141113772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.1088/1361-6501/ad4e57
K. Karioja, Riku-Pekka Nikula, Juhani Nissilä
Various methods are used in the field of machine diagnostics for recognizing cyclostationarity in signals. The real order derivatives of vibration signals, however, have been rarely reported from the perspective of their effect on the performance of cyclostationarity detection methods. In this paper, we use real order derivatives together with spectral correlation, spectral coherence and squared envelope. Our results suggest that adjusting the order of derivative can enhance the analysis outcome of spectral correlation and squared envelope in particular. Remarkably, the results also suggest that squared envolope, when used alongside real-order derivatives, may replace spectral correlation and spectral coherence. This approach allows obtaining results with reduced computational power, making it advantageous for applications like industrial edge computing where cost-effective hardware is crucial.
{"title":"Cyclostationarity and real order derivatives in roller bearing fault detection","authors":"K. Karioja, Riku-Pekka Nikula, Juhani Nissilä","doi":"10.1088/1361-6501/ad4e57","DOIUrl":"https://doi.org/10.1088/1361-6501/ad4e57","url":null,"abstract":"\u0000 Various methods are used in the field of machine diagnostics for recognizing cyclostationarity in signals. The real order derivatives of vibration signals, however, have been rarely reported from the perspective of their effect on the performance of cyclostationarity detection methods. In this paper, we use real order derivatives together with spectral correlation, spectral coherence and squared envelope. Our results suggest that adjusting the order of derivative can enhance the analysis outcome of spectral correlation and squared envelope in particular. Remarkably, the results also suggest that squared envolope, when used alongside real-order derivatives, may replace spectral correlation and spectral coherence. This approach allows obtaining results with reduced computational power, making it advantageous for applications like industrial edge computing where cost-effective hardware is crucial.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"134 47","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141115184","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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