Abstract A stable and reliable time keeping system depends on the integrity monitoring of the atomic frequency standard. This paper reports a scheme for autonomous integrity monitoring of multiple atomic clocks, which combines the frequency standard comparison method and the frequency jump detection method. The frequency standard comparison method uses multi-channel synchronous acquisition technology and digital frequency measurement technology to realize the precise measurement of multiple atomic frequency standards. The frequency jump detection method uses adaptive filtering to predict the relative frequency difference and give an accurate and timely alarm for the abnormal of frequency jump. The results show that the noise floor frequency standard comparator is better than 6.5×10−15 s. For a relative frequency deviation of 2.0×10−6 Hz, the probability of anomaly detection is almost 100 %. The system has high frequency measurement resolution and fast alarm of frequency jump, which can meet the real-time requirements of a time keeping system for the integrity monitoring of multiple atomic clocks.
{"title":"A Study on Autonomous Integrity Monitoring of Multiple Atomic Clocks","authors":"Bo Xiao, Ya Liu, Yanrong Xue, Xiaohui Li","doi":"10.2478/msr-2022-0025","DOIUrl":"https://doi.org/10.2478/msr-2022-0025","url":null,"abstract":"Abstract A stable and reliable time keeping system depends on the integrity monitoring of the atomic frequency standard. This paper reports a scheme for autonomous integrity monitoring of multiple atomic clocks, which combines the frequency standard comparison method and the frequency jump detection method. The frequency standard comparison method uses multi-channel synchronous acquisition technology and digital frequency measurement technology to realize the precise measurement of multiple atomic frequency standards. The frequency jump detection method uses adaptive filtering to predict the relative frequency difference and give an accurate and timely alarm for the abnormal of frequency jump. The results show that the noise floor frequency standard comparator is better than 6.5×10−15 s. For a relative frequency deviation of 2.0×10−6 Hz, the probability of anomaly detection is almost 100 %. The system has high frequency measurement resolution and fast alarm of frequency jump, which can meet the real-time requirements of a time keeping system for the integrity monitoring of multiple atomic clocks.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"202 - 207"},"PeriodicalIF":0.9,"publicationDate":"2022-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42786998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Down syndrome (DS) or Trisomy 21 is a genetic disorder that causes intellectual and mental disability in fetuses. The most essential marker for detecting DS during the first trimester of pregnancy is nuchal translucency (NT). Effective segmentation of the NT contour from the ultrasound (US) images becomes challenging due to the presence of speckle noise and weak edges. This study presents a Convolutional Neural Network (CNN) based SegNet model using a Visual Geometry Group (VGG-16) for semantically segmenting the NT region from the US fetal images and providing a fast and affordable diagnosis during the early stages of gestation. A transfer learning approach using AlexNet is implemented to train the NT segmented regions for the identification of DS. The proposed model achieved a Jaccard index of 0.96 and classification accuracy of 91.7 %, sensitivity of 85.7 %, and a Receiver operating characteristic (ROC) of 0.95.
{"title":"Deep Learning Measurement Model to Segment the Nuchal Translucency Region for the Early Identification of Down Syndrome","authors":"M. C. Thomas, S. Arjunan","doi":"10.2478/msr-2022-0023","DOIUrl":"https://doi.org/10.2478/msr-2022-0023","url":null,"abstract":"Abstract Down syndrome (DS) or Trisomy 21 is a genetic disorder that causes intellectual and mental disability in fetuses. The most essential marker for detecting DS during the first trimester of pregnancy is nuchal translucency (NT). Effective segmentation of the NT contour from the ultrasound (US) images becomes challenging due to the presence of speckle noise and weak edges. This study presents a Convolutional Neural Network (CNN) based SegNet model using a Visual Geometry Group (VGG-16) for semantically segmenting the NT region from the US fetal images and providing a fast and affordable diagnosis during the early stages of gestation. A transfer learning approach using AlexNet is implemented to train the NT segmented regions for the identification of DS. The proposed model achieved a Jaccard index of 0.96 and classification accuracy of 91.7 %, sensitivity of 85.7 %, and a Receiver operating characteristic (ROC) of 0.95.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"187 - 192"},"PeriodicalIF":0.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41800097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Utomo, N. Kusnandar, H. Firdaus, Intan Paramudita, I. Kasiyanto, Q. Lailiyah, W. Syam
Abstract The paper presents the comparison of uncertainty measurement estimations of the energy performances of gas stoves. The Guide to the Expression of Uncertainty in Measurement (GUM) framework and two Monte Carlo Simulation (MCM) approaches: ordinary and adaptive MCM were applied for the energy performance uncertainty: thermal energy and efficiency measurement uncertainties. The validation of the two MCMs is performed by comparing the MCM estimations to the GUM estimations for the thermal energy and efficiency measurement results. A test method designed in Indonesia National Standard SNI 7368:2011 was employed for the thermal energy and efficiency determinations. The results of the GUM and two MCM methods are in good agreement for the estimation of the thermal energy value. Significant differences of the uncertainty estimations for the thermal energy and efficiency results are observed for both GUM and MCM methods. Both the ordinary and adaptive MCM estimations give larger coverage interval compared to the GUM method. The adaptive MCM can give similar estimations with a much lower number of iterations compared to the ordinary MCM. From the estimation difference between the GUM and MCM methods, suggestions are needed for the improvement in measurement models for thermal energy and efficiency of the standard.
{"title":"Comparison of GUM and Monte Carlo Methods for Measurement Uncertainty Estimation of the Energy Performance Measurements of Gas Stoves","authors":"B. Utomo, N. Kusnandar, H. Firdaus, Intan Paramudita, I. Kasiyanto, Q. Lailiyah, W. Syam","doi":"10.2478/msr-2022-0020","DOIUrl":"https://doi.org/10.2478/msr-2022-0020","url":null,"abstract":"Abstract The paper presents the comparison of uncertainty measurement estimations of the energy performances of gas stoves. The Guide to the Expression of Uncertainty in Measurement (GUM) framework and two Monte Carlo Simulation (MCM) approaches: ordinary and adaptive MCM were applied for the energy performance uncertainty: thermal energy and efficiency measurement uncertainties. The validation of the two MCMs is performed by comparing the MCM estimations to the GUM estimations for the thermal energy and efficiency measurement results. A test method designed in Indonesia National Standard SNI 7368:2011 was employed for the thermal energy and efficiency determinations. The results of the GUM and two MCM methods are in good agreement for the estimation of the thermal energy value. Significant differences of the uncertainty estimations for the thermal energy and efficiency results are observed for both GUM and MCM methods. Both the ordinary and adaptive MCM estimations give larger coverage interval compared to the GUM method. The adaptive MCM can give similar estimations with a much lower number of iterations compared to the ordinary MCM. From the estimation difference between the GUM and MCM methods, suggestions are needed for the improvement in measurement models for thermal energy and efficiency of the standard.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"160 - 169"},"PeriodicalIF":0.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41790794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper proposes a new diagnosis technique for predicting the big data of roller bearing multi-level fault, which uses the deep learning method for the feature representation of the vibration signal and an optimized machine learning model. First, vibration feature extraction by stacked auto-encoders (VFE-SAE) with two layers in roller bearing fault signals is proposed. The unsupervised learning algorithm in VFE-SAE is used to reveal significant properties in the vibration data, such as nonlinear and non-stationary properties. The extracted features can provide good discriminability for fault diagnosis tasks. Second, a classifier model is optimized based on least squares support vector machine classification and particle swarm optimization (LSSVM-PSO). This model is used to perform supervised fine-tuning and classification; it is trained with the labelled features to identify the target data. Especially, using transfer learning, the performance of the bearing fault diagnosis technique can be fine-tuned. In other words, the features of the target vibration signal can be extracted by the learning of feature representation, which is dependent on the weight matrix of hidden layers of the VFE-SAE method. The experimental results (by analyzing the roller bearing vibration signals with multi-status fault) demonstrate that VFE-SAE based feature extraction in conjunction with the LSSVM-PSO classification is more accurate than other popular classifier models. The proposed VFE-SAE – LSSVMPSO method can effectively diagnose bearing faults with 97.76 % accuracy, even when using 80 % of the target data.
{"title":"Stacked Auto-encoder Based Feature Transfer Learning and Optimized LSSVM-PSO Classifier in Bearing Fault Diagnosis","authors":"V. Nguyen, Junsheng Cheng, V. Thai","doi":"10.2478/msr-2022-0022","DOIUrl":"https://doi.org/10.2478/msr-2022-0022","url":null,"abstract":"Abstract This paper proposes a new diagnosis technique for predicting the big data of roller bearing multi-level fault, which uses the deep learning method for the feature representation of the vibration signal and an optimized machine learning model. First, vibration feature extraction by stacked auto-encoders (VFE-SAE) with two layers in roller bearing fault signals is proposed. The unsupervised learning algorithm in VFE-SAE is used to reveal significant properties in the vibration data, such as nonlinear and non-stationary properties. The extracted features can provide good discriminability for fault diagnosis tasks. Second, a classifier model is optimized based on least squares support vector machine classification and particle swarm optimization (LSSVM-PSO). This model is used to perform supervised fine-tuning and classification; it is trained with the labelled features to identify the target data. Especially, using transfer learning, the performance of the bearing fault diagnosis technique can be fine-tuned. In other words, the features of the target vibration signal can be extracted by the learning of feature representation, which is dependent on the weight matrix of hidden layers of the VFE-SAE method. The experimental results (by analyzing the roller bearing vibration signals with multi-status fault) demonstrate that VFE-SAE based feature extraction in conjunction with the LSSVM-PSO classification is more accurate than other popular classifier models. The proposed VFE-SAE – LSSVMPSO method can effectively diagnose bearing faults with 97.76 % accuracy, even when using 80 % of the target data.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"177 - 186"},"PeriodicalIF":0.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43703324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper presents an error analysis of the estimation of energy meter correction factor (CF) using a remote non-invasive technique. A method of the CF estimation based on the comparison of synchronously detected power steps in power consumption profiles of meter under test and reference meter is elaborated. The dependence of meter CF estimation uncertainty upon the magnitude of power steps, the number of power steps per observation interval, and the number of meters under test monitored by one reference meter is approximated. The synthesized consumer active power profiles are used to obtain training data points that are fit by these approximating equations.
{"title":"Estimation of Energy Meter Accuracy using Remote Non-invasive Observation","authors":"M. Saunoris, Ž. Nakutis, M. Knyva","doi":"10.2478/msr-2022-0021","DOIUrl":"https://doi.org/10.2478/msr-2022-0021","url":null,"abstract":"Abstract This paper presents an error analysis of the estimation of energy meter correction factor (CF) using a remote non-invasive technique. A method of the CF estimation based on the comparison of synchronously detected power steps in power consumption profiles of meter under test and reference meter is elaborated. The dependence of meter CF estimation uncertainty upon the magnitude of power steps, the number of power steps per observation interval, and the number of meters under test monitored by one reference meter is approximated. The synthesized consumer active power profiles are used to obtain training data points that are fit by these approximating equations.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"170 - 176"},"PeriodicalIF":0.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44054275","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The miniature sensor devices and power-efficient Body Area Networks (BANs) for Human Activity Recognition (HAR) have gained increasing interest in different fields, including Daily Life Assistants (DLAs), medical treatment, sports analysis, etc. The HAR systems normally collect data with wearable sensors and implement the computational tasks with a host machine, where real-time transmission and processing of sensor data raise a challenge for both the network and the host machine. This investigation focuses on the hardware/software co-design for optimized sensing and computing of wearable HAR sensor networks. The contributions include (1) design of a miniature wearable sensor node integrating a Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS IMU) with a Bluetooth Low Energy (BLE) in-built Micro-Control Unit (MCU) for unobtrusive wearable sensing; (2) task-centric optimization of the computation by shifting data pre-processing and feature extraction to sensor nodes for in-situ computing, which reduces data transmission and relieves the load of the host machine; (3) optimization and evaluation of classification algorithms Particle Swarm Optimization-based Support Vector Machine (PSO-SVM) and Cross Validation-based K-Nearest Neighbors (CV-KNN) for HAR with the presented techniques. Finally, experimental studies were conducted with two sensor nodes worn on the wrist and elbow to verify the effectiveness of the recognition of 10 virtual handwriting activities, where 10 recruited participants each repeated an activity 5 times. The results demonstrate that the proposed system can implement HAR tasks effectively with an accuracy of 99.20 %.
{"title":"Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network","authors":"Mingxing Zhang, Hongpeng Li, Tian Ge, Zhaozong Meng, N. Gao, Zonghua Zhang","doi":"10.2478/msr-2022-0024","DOIUrl":"https://doi.org/10.2478/msr-2022-0024","url":null,"abstract":"Abstract The miniature sensor devices and power-efficient Body Area Networks (BANs) for Human Activity Recognition (HAR) have gained increasing interest in different fields, including Daily Life Assistants (DLAs), medical treatment, sports analysis, etc. The HAR systems normally collect data with wearable sensors and implement the computational tasks with a host machine, where real-time transmission and processing of sensor data raise a challenge for both the network and the host machine. This investigation focuses on the hardware/software co-design for optimized sensing and computing of wearable HAR sensor networks. The contributions include (1) design of a miniature wearable sensor node integrating a Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS IMU) with a Bluetooth Low Energy (BLE) in-built Micro-Control Unit (MCU) for unobtrusive wearable sensing; (2) task-centric optimization of the computation by shifting data pre-processing and feature extraction to sensor nodes for in-situ computing, which reduces data transmission and relieves the load of the host machine; (3) optimization and evaluation of classification algorithms Particle Swarm Optimization-based Support Vector Machine (PSO-SVM) and Cross Validation-based K-Nearest Neighbors (CV-KNN) for HAR with the presented techniques. Finally, experimental studies were conducted with two sensor nodes worn on the wrist and elbow to verify the effectiveness of the recognition of 10 virtual handwriting activities, where 10 recruited participants each repeated an activity 5 times. The results demonstrate that the proposed system can implement HAR tasks effectively with an accuracy of 99.20 %.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"193 - 201"},"PeriodicalIF":0.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46473263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Flame combustion diagnostics is a technique that uses different methods to diagnose the flame combustion process and study its physical and chemical basis. As one of the most important parameters of the combustion process, the flame equivalence ratio has a significant influence on the entire flame combustion, especially on the combustion efficiency and the emission of pollutants. Therefore, the measurement of the flame equivalence ratio has a huge impact on efficient combustion and environment protection. In view of this, several effective measuring methods were proposed, which were based on the different characteristics of flames radicals such as spectral properties. With the rapid growth of machine learning, more and more scholars applied it in the combustion diagnostics due to the excellent ability to fit parameters. This paper presents a review of various measuring techniques of hydrocarbon flame equivalent ratio and the applications of machine learning in combustion diagnostics, finally making a brief comparison between different measuring methods.
{"title":"Review of Measurement Techniques of Hydrocarbon Flame Equivalence Ratio and Applications of Machine Learning","authors":"Hao Yang, Yuwen Fu, Jiansheng Yang","doi":"10.2478/msr-2022-0016","DOIUrl":"https://doi.org/10.2478/msr-2022-0016","url":null,"abstract":"Abstract Flame combustion diagnostics is a technique that uses different methods to diagnose the flame combustion process and study its physical and chemical basis. As one of the most important parameters of the combustion process, the flame equivalence ratio has a significant influence on the entire flame combustion, especially on the combustion efficiency and the emission of pollutants. Therefore, the measurement of the flame equivalence ratio has a huge impact on efficient combustion and environment protection. In view of this, several effective measuring methods were proposed, which were based on the different characteristics of flames radicals such as spectral properties. With the rapid growth of machine learning, more and more scholars applied it in the combustion diagnostics due to the excellent ability to fit parameters. This paper presents a review of various measuring techniques of hydrocarbon flame equivalent ratio and the applications of machine learning in combustion diagnostics, finally making a brief comparison between different measuring methods.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"122 - 135"},"PeriodicalIF":0.9,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49226636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The processing speed of the trained YOLACT reaches 30 frames per second. Compared with other segmentation methods, the proposed model provides excellent performance in both segmentation and detection accuracy. Our efficient deep learning-based system may support the advancement of non-contact defect assessment methods for quality control of the screw manufacture.
摘要在金属螺钉的生产过程中，通常采用人工方法进行缺陷检测。由于深度学习在视觉检测领域大放异彩，本研究采用You Only Look At CoefficienTs (YOLACT)算法对金属螺钉头表面缺陷进行检测。利用显微相机自动扫描系统采集不同缺陷的原始图像，建立训练和验证数据集。实验结果表明，训练后的YOLACT足以达到92.8%的平均准确率，并且缺失率和误报率都很低。训练后的YOLACT处理速度达到每秒30帧。与其他分割方法相比，该模型在分割和检测精度方面都有较好的表现。我们高效的基于深度学习的系统可以为螺杆制造质量控制的非接触缺陷评估方法的发展提供支持。
{"title":"Real-Time Instance Segmentation of Metal Screw Defects Based on Deep Learning Approach","authors":"Wei-Yu Chen, Yu-Reng Tsao, Jin-Yi Lai, Ching-Jung Hung, Yu-Cheng Liu, Cheng-Yang Liu","doi":"10.2478/msr-2022-0014","DOIUrl":"https://doi.org/10.2478/msr-2022-0014","url":null,"abstract":"Abstract In general, manual methods are often used to inspect defects in the production of metal screws. As deep learning shines in the field of visual detection, this study employs the You Only Look At CoefficienTs (YOLACT) algorithm to detect the surface defects of the metal screw heads. The raw images with different defects are collected by an automated microscopic camera scanning system to build the training and validation datasets. The experimental results demonstrate that the trained YOLACT is sufficient to achieve a mean average accuracy of 92.8 % with low missing and false rates. The processing speed of the trained YOLACT reaches 30 frames per second. Compared with other segmentation methods, the proposed model provides excellent performance in both segmentation and detection accuracy. Our efficient deep learning-based system may support the advancement of non-contact defect assessment methods for quality control of the screw manufacture.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"107 - 111"},"PeriodicalIF":0.9,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42356171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract With the fast growth of wireless communications between nodes and sensor units and the increase of devices installed in remote places, and the development of IIoT applications, new requirements for power energy supply are needed to assure device functionality and data communication capabilities during extended periods of time. For these applications, energy harvesting takes place as a good solution to increase the autonomy of remote measuring solutions, since the usage of conventional power supply solutions has clear limitations in terms of equipment access and increased maintenance costs. In this context, regenerative energy sources such as thermoelectric, magnetic and piezoelectric based, as well as renewable energy sources, such as photovoltaic and wind based, among others, make the development of different powering solutions for remote sensing units possible. The main purpose of this paper is to present a flexible testing platform to characterize piezoelectric devices and to evaluate their performance in terms of harvesting energy. The power harvesting solutions are focused on converting the energy from mechanical vibrations, provided by different types of equipment and mechanical structures, to electrical energy. This study is carried out taking into account the power supply capabilities of piezoelectric devices as a function of the amplitude, frequency and spectral contents of the vibration stimulus. Several experimental results using, as an example, a specific piezoelectric module, are included in the paper.
{"title":"An Integrated Testing Solution for Piezoelectric Sensors and Energy Harvesting Devices","authors":"José Dias Pereira, M. Alves","doi":"10.2478/msr-2022-0013","DOIUrl":"https://doi.org/10.2478/msr-2022-0013","url":null,"abstract":"Abstract With the fast growth of wireless communications between nodes and sensor units and the increase of devices installed in remote places, and the development of IIoT applications, new requirements for power energy supply are needed to assure device functionality and data communication capabilities during extended periods of time. For these applications, energy harvesting takes place as a good solution to increase the autonomy of remote measuring solutions, since the usage of conventional power supply solutions has clear limitations in terms of equipment access and increased maintenance costs. In this context, regenerative energy sources such as thermoelectric, magnetic and piezoelectric based, as well as renewable energy sources, such as photovoltaic and wind based, among others, make the development of different powering solutions for remote sensing units possible. The main purpose of this paper is to present a flexible testing platform to characterize piezoelectric devices and to evaluate their performance in terms of harvesting energy. The power harvesting solutions are focused on converting the energy from mechanical vibrations, provided by different types of equipment and mechanical structures, to electrical energy. This study is carried out taking into account the power supply capabilities of piezoelectric devices as a function of the amplitude, frequency and spectral contents of the vibration stimulus. Several experimental results using, as an example, a specific piezoelectric module, are included in the paper.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"100 - 106"},"PeriodicalIF":0.9,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48524799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Residual stress measurement in different sorts of mechanical and mechatronic objects has become an important part of the designing process and following maintenance. Therefore, a sufficient experimental method could significantly increase the accuracy and reliability of the evaluation process. Ring-Core method is a well-known semi-destructive method, yet it is still not standardized. This work tries to improve the evaluation process of the Ring-Core method by analyzing the influence of the necessary geometric parameters of the investigated object. Subsequently, residual stress computation accuracy is increased by proposed recommendations.
{"title":"Complex Analysis of the Necessary Geometric Parameters of the Tested Component in the Ring-Core Evaluation Process","authors":"Patrik Šarga, A. Galajdová, M. Vagaš, F. Menda","doi":"10.2478/msr-2022-0017","DOIUrl":"https://doi.org/10.2478/msr-2022-0017","url":null,"abstract":"Abstract Residual stress measurement in different sorts of mechanical and mechatronic objects has become an important part of the designing process and following maintenance. Therefore, a sufficient experimental method could significantly increase the accuracy and reliability of the evaluation process. Ring-Core method is a well-known semi-destructive method, yet it is still not standardized. This work tries to improve the evaluation process of the Ring-Core method by analyzing the influence of the necessary geometric parameters of the investigated object. Subsequently, residual stress computation accuracy is increased by proposed recommendations.","PeriodicalId":49848,"journal":{"name":"Measurement Science Review","volume":"22 1","pages":"136 - 142"},"PeriodicalIF":0.9,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42250214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：