Measurement Science and Technology最新文献

{"title":"A domain adaptation network with feature scale preservation for remaining useful life prediction of rolling bearings under variable operating conditions","authors":"D. She, Hu Wang, Hongfei Zhang, Jin Chen","doi":"10.1088/1361-6501/ad1918","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1918","url":null,"abstract":"Transfer learning and domain adaptation (DA) methods have been utilized in bearing prognostic and health management, but most of the current DA methods do not take into account the feature scale change of degraded features when aligning the feature distribution, and these methods are more suitable for the classification problem, which is more robust to the feature scale change. However, they perform poorly in regression problems. In addition, most of the remaining useful life (RUL) prediction methods require preprocessing such as statistical feature extraction on the signal, which makes the prediction process complicated. To solve the above problems, a DA method based on the representation subspace distance (RSD) is proposed for predicting the bearing RUL under different operating conditions. First, the proposed convolutional neural network (CNN) self-attention (SA) long short term memory network model is utilized to extract the deep features from the original signal, which overcomes the limitations of the CNN in extracting time series. Then, the RSD in the Riemannian geometry of the Grassmann manifold is proposed as a domain transfer loss to learn domain invariant features. The modified method can align the feature distribution of the source domain and the target domain without changing the feature scale. At the same time, the bases mismatch penalization is introduced to avoid destroying the semantic information of the features in the process of domain alignment. Finally, the effectiveness of the proposed method is verified by experiments on four types of transfer tasks, and its superiority is also demonstrated by comparison with other advanced methods.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"64 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cyclic voltammetry signal analyses for equivalent electric circuits consisting of multiple resistors and capacitors","authors":"T. I. Wong, Xiaodong Zhou","doi":"10.1088/1361-6501/ad1b32","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1b32","url":null,"abstract":"\u0000 An equivalent circuit is a combination of resistors, capacitors, inductors, Warburg impedance or constant phase elements which is widely measured and analyzed to understand electrochemical (EC) properties of materials such as batteries, thin films, or corrosion. An equivalent electric circuit is also built as different dummy cells to evaluate the measurement accuracy of EC instruments by key detection modes including electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV). Dummy cell signals for EIS can be easily understood and simulated by existing EIS signal analysis software, while dummy cell signals for CV curves have no simulation formula so far. When using dummy cells for CV tests, users can only observe if the CV curves obtained seem to be correct, which impairs the accuracy and efficiency of quality as well as functionality judgement of an EC instrument. In this study, we conducted CV signal analyses on 5 cases of equivalent circuits consisting of multiple resistors and capacitors, with their correctness verified by dummy cell experiments using two different potentiostats. Based on the measured CV signals, we further demonstrated a data process method to judge the performance of a potentiostat quantitatively and automatically. This study enables researchers to calculate CV curves of resistors and capacitors formed EC equivalent circuits and provides a quantitative method to verify if an EC instrument is functioning well.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"37 8","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139385092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DECVAE: Data augmentation via conditional variational auto-encoder with distribution enhancement for few-shot fault diagnosis of mechanical system","authors":"Yikun Liu, Song Fu, Lin Lin, Sihao Zhang, Shiwei Suo, Jianjun Xi","doi":"10.1088/1361-6501/ad197c","DOIUrl":"https://doi.org/10.1088/1361-6501/ad197c","url":null,"abstract":"Conditional variational autoencoder (CVAE) has the potential for few-sample fault diagnosis of mechanical systems. Nevertheless, the scarcity of faulty samples leads the augmented samples generated using CVAE suffer from limited diversity. To address the issue, a novel CVAE variant namely CVAE with distribution augmentation (DECVAE) is developed, to generate a set of high-quality augmented samples that are different but share very similar characteristics and categories with the corresponding real samples. First, DECVAE add a new sample distribution distance loss into the optimization objective of traditional CVAE. Amplifying this loss in training process can make the augmented samples cover a larger space, thereby improving diversity. Second, DECVAE introduces an auxiliary classifier into traditional CVAE to enhance the sensitivity to category information, keeping the augmented samples class invariance. Furthermore, to ensure that the information of edge-distributed samples can be fully learned and make augmented samples representative and authentic, a novel multi-model independent fine-tuning strategy is designed to train the DECVAE, which utilizes multiple independent models to fairly focus on all samples of the minority class during DECVAE training. Finally, the effectiveness of the developed DECVAE in few-shot fault diagnosis of mechanical systems is verified on a series of comparative experiments.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"2 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139385905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An improved attitude estimation algorithm for suppressing magnetic vector disturbance based on extended Kalman filter","authors":"Yikai Zong, Shujing Su, Yuhong Gao, Lili Zhang","doi":"10.1088/1361-6501/ad1917","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1917","url":null,"abstract":"This paper proposes an improved attitude estimation algorithm based on the extended Kalman filter (EKF), and it is applied to suppress the accuracy reduction in attitude estimation caused by fusing magnetometer data under large angular motion. In the proposed attitude estimation structure, the approximate variance of the estimated horizontal northbound magnetic vector is used to dynamically adjust the participation of magnetometer data in attitude estimation, as the approximate variance increases significantly under large angular motion and fusing magnetometer data will reduce estimation accuracy. A three-axis position-velocity controlled turntable is used to conduct rocking experiments for validating the proposed attitude estimation algorithm. The results show a significant improvement in yaw angle estimation accuracy with the proposed attitude estimation algorithm and correspondingly enhance the distribution of pitch and roll angle errors.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"18 6","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139385712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study of the fault diagnosis method for gas turbine sensors based on inter-parameter coupling information","authors":"Yuzhang Wang, Kanru Cheng, Fan Liu, Jiao Li, Kunyu Zhang","doi":"10.1088/1361-6501/ad1914","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1914","url":null,"abstract":"Correct and reliable measurement data are crucial for state monitoring, safe operations, health assessment, and life prediction of integrated energy systems (IESs). Sensors are often installed in harsh environments and prone to all kinds of faults; therefore, it is necessary to diagnose sensor faults. A diagnostic method for sensor faults based on gradient histogram distribution (GHD) combined with light gradient boosting machine (LightGBM) is presented in this paper. This proposed method effectively utilizes the coupling information between the relevant parameters. The GHD efficiently extracted the time-domain characteristics of sensor faults and reduced the dimension of eigenvectors. This is beneficial to increasing the diagnostic speed. The kernel density estimation distributions of the gradient and eigenvectors for the sensor with strong correlation are similar, but that for the sensor with weak correlation are completely different. A LightGBM classifier trained based on the feature vectors was utilized to diagnose and classify the sensor faults. The diagnosis accuracy and the diagnosis time of this developed method were examined using the multiple-condition practical operation data of gas turbines in the IES. The experiment results demonstrate that the diagnostic accuracy of five sensor faults using this developed method is all above 90%. The diagnostic time is about 0.47–1.34 s, and is less than 2 s for the gradual faults.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"23 9","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139450777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Contribution of BDS-3 observations to the precise orbit determination of LEO satellites: A case study of TJU-01","authors":"Kai Wei, Min Li, Tianhe Xu, Dixing Wang, Yali Shi, Honglei Yang, Xiaoji Dai","doi":"10.1088/1361-6501/ad1b33","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1b33","url":null,"abstract":"\u0000 The precise orbit determination (POD) of scientific low Earth orbit (LEO) satellites is a prerequisite for the successful implementation of scientific missions. In recent years, global navigation satellite systems have become the main means of determining the orbits of LEO satellites. The global navigation satellite system receiver onboard the Tianjin University No. 1 (TJU-01) satellite receives both GPS and BDS-2/3 signals, with the addition of BDS-2/3 observations playing an important role in improving the POD of LEO satellites. This study comprehensively analyzes the spaceborne GPS/BDS data quality, including BDS-2/3 and GPS code multipath errors. Appreciable code multipath errors are found for the B1I signal of BDS-2 medium Earth orbit (MEO) satellites at elevations higher than 40°, whereas slight near-field relevant multipath errors of both frequencies are found for GPS and BDS-3 MEO satellites. The GPS and BDS-2/3 code multipath errors are estimated through elevation/azimuth-relevant piece-wise modeling and applied in the POD calculations. Several schemes, namely GPS-based, BDS-based, BDS-based without geo-synchronous (GEO) satellites, and GPS/BDS combined schemes, are designed to evaluate the POD performance. Fourteen days of data are calculated and the average three-dimensional (3D) orbital root mean square (RMS) of orbit overlapping differences obtained from GPS-based and BDS-based POD (without GEO satellites) solutions are 37.4 and 27.1 mm, respectively. The BDS-based solutions are obviously better than the GPS-based solutions, mainly owing to better data availability. The GPS/BDS combined solutions have the best accuracy, with a 3D RMS value of 20.6 mm. In addition, when BDS GEO satellites are included, the 3D RMS of the overlapping orbit differences reduces to 32.9 and 27.4 mm for BDS-based and GPS/BDS combined solutions, respectively. Double-difference (DD) and single-difference (SD) integer ambiguity resolution (IAR) are adopted to further improve the POD performance. The fixed orbit of the TJU-01 satellite is solved through DD IAR and SD IAR, and the contribution of the TJU-01 satellite to ambiguity fixing is analyzed. Relative to the float solution, the improvements made using the two ambiguity fixing approaches are equivalent, both being approximately 13%. The importance of this research is not only the precise determination of the orbit of TJU-01 for occultation service but also the demonstration of the contribution of BDS observations to the performance of the POD of LEO satellites.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"64 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139385538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A discriminative multiscale feature extraction network for facial expression recognition in the wild","authors":"Xiaoyu Wen, Juxiang Zhou, Jianhou Gan, Sen Luo","doi":"10.1088/1361-6501/ad191c","DOIUrl":"https://doi.org/10.1088/1361-6501/ad191c","url":null,"abstract":"Driven by advancements in deep learning technologies, substantial progress has been achieved in the field of facial expression recognition over the past decade, while challenges remain brought about by occlusions, pose variations and subtle expression differences in unconstrained (wild) scenarios. Therefore, a novel multiscale feature extraction method is proposed in this paper, that leverages convolutional neural networks to simultaneously extract deep semantic features and shallow geometric features. Through the mechanism of channel-wise self-attention, prominent features are further extracted and compressed, preserving advantageous features for distinction and thereby reducing the impact of occlusions and pose variations on expression recognition. Meanwhile, inspired by the large cosine margin concept used in face recognition, a center cosine loss function is proposed to avoid the misclassification caused by the underlying interclass similarity and substantial intra-class feature variations in the task of expression recognition. This function is designed to enhance the classification performance of the network through making the distribution of samples within the same class more compact and that between different classes sparser. The proposed method is benchmarked against several advanced baseline models on three mainstream wild datasets and two datasets that present realistic occlusion and pose variation challenges. Accuracies of 89.63%, 61.82%, and 91.15% are achieved on RAF-DB, AffectNet and FERPlus, respectively, demonstrating the greater robustness and reliability of this method compared to the state-of-the-art alternatives in the real world.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"42 11","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139384971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel monitoring method based on multi-model information extraction and fusion","authors":"Zhichao Li, Mingxue Shen, Li Tian, Xue-feng Yan","doi":"10.1088/1361-6501/ad1a87","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1a87","url":null,"abstract":"\u0000 Modern industrial processes are increasingly complex, where multiple characteristics usually coexist in process data. Therefore, traditional monitoring methods based on a single model may ignore other data characteristics and obtain poor monitoring performance. Aiming at this problem, a novel monitoring method based on multi-model information extraction and fusion is proposed in this paper. Firstly, several methods are used to extract different characteristics from process data. For example, principal component analysis, independent component analysis and slow features analysis can be used to extract Gaussian, non-Gaussian and dynamic characteristics respectively. Secondly, features extracted from multiple models are combined into new potential features. Then, Lasso regression models between potential features and process variables are established. In this way, not only are multiple characteristics in process data considered during the reconstruction, but key potential features (KPFs) can be selected for each process variable. The KPFs for each process variable can form a monitoring subspace to enhance the sensitivity for fault detection. Furthermore, cluster analysis is used to reduce the redundancy of monitoring subspaces based on the similarity of each subspace. Process monitoring can be achieved by fusing the monitoring results of finally determined multiple subspaces and residual space. Case studies on three simulation processes and a real industrial process demonstrate the effectiveness and better performance.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"20 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139389428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influencing factors and uncertainty analysis for Kerr electro-optic effect based electric field measurements in transformer oil under impulse voltage","authors":"C. Gao, Xiaolin Zhao, Shuqi Zhang, Ke Wang, Bo Qi, Chengrong Li","doi":"10.1088/1361-6501/ad1a68","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1a68","url":null,"abstract":"\u0000 The design of insulating structures for transformers under impulse voltage relies predominantly on simulation software due to the absence of experimental validation. This underscores the pressing need for comprehensive research into the spatial electric field and charge properties of oil-paper/pressboard insulation systems. In response to this imperative, a suite of specialized instruments leveraging the Kerr electro-optic effect to meticulously measure the spatial electric field within oil-pressboard structures under impulse voltage was established. As the precision of measurements hinges upon a multitude of influencing factors, this study embarks on a multifaceted examination, centering its focus on four pivotal dimensions: incident laser beam angle, electrical noise, temperature and non-ideal optical elements. A quantitative calculation method for electric field measurement errors was presented, and on the basis of which, suppression methods are proposed for the error sources having the largest impacts on the experimental results. Finally, the overall measurement uncertainty of the device is systematically evaluated.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"29 24","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139388741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved lightweight federated learning network for fault feature extraction of reciprocating machinery","authors":"Junling Zhang, Lixiang Duan, Ke Li, Shilong Luo","doi":"10.1088/1361-6501/ad1a69","DOIUrl":"https://doi.org/10.1088/1361-6501/ad1a69","url":null,"abstract":"\u0000 he working environment of reciprocating machinery is complex, characterized by nonlinear and non-stationary signals. Deep learning can solve the above problems, but it has its own problems such as complex model and large amount of parameters. Additionally, privacy considerations among enterprises prevent data sharing, leading to the emergence of \"data islands\" and inadequate training of deep learning models. Based on the above analysis, this paper proposes a reciprocating mechanical feature extraction method based on an improved federated lightweight network. A lightweight network SqueezeNet model is used to solve the problems such as long training time of deep learning. By establishing a federated learning framework, the reciprocating mechanical data can be collectively diagnosed across various enterprises, thereby addressing the problem of limited model training caused by insufficient data. Furthermore, to enhance the accuracy of network training and diagnosis, modifications are made to the SqueezeNet network to reduce the number of model parameters while increasing the number and variety of feature extractions. Experimental results demonstrate that when the number of 1×1 and 3×3 channels is 1 to 7, the fault diagnosis accuracy is the highest, about 97.96%, which enriches the categories of feature extraction. The number of parameters in In-SqueezeNet is 56% of that in SqueezeNet network model, and the training time is reduced by nearly 15%. The fault diagnosis accuracy is increased from 95.1% to 97.3%, and the diversity of extracted features is increased. Compared with other network models such as ResNet, the improved lightweight federated learning network has a fault diagnosis accuracy of 96.6%, an improvement of 10.6%. At the same time, the training time was reduced to 1982s, a reduction of about 41.5%. The validity of the proposed model is further verified.","PeriodicalId":18526,"journal":{"name":"Measurement Science and Technology","volume":"44 21","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139451990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}