Measurement最新文献_第10页

Investigation on Machine learning based fault detection and estimation in hydro turbines of industrial hydro power plant

IF 5.2 2区工程技术

Measurement Pub Date : 2025-01-25 DOI: 10.1016/j.measurement.2025.116852

V. Sujatha

{"title":"Investigation on Machine learning based fault detection and estimation in hydro turbines of industrial hydro power plant","authors":"V. Sujatha","doi":"10.1016/j.measurement.2025.116852","DOIUrl":"10.1016/j.measurement.2025.116852","url":null,"abstract":"<div><div>An industrial plant safety depends on identification, detection and estimation of faults. Appropriate fault diagnosis can result in higher productivity in process industries. Real-time fault detection is achieved using data-driven modelling of an industrial hydro power plant. It is still very difficult to simulate complicated and nonlinear systems, like hydro turbines, despite efforts to detect faults. The industrial power plant at Mettur was modelled in order to gather the data set. This article focusses on mathematical modelling of nonlinear hydro-turbines. Also, investigates the application of machine learning algorithms to detect and estimate faults in hydro turbines, which are critical components in industrial hydro power plants. The goal is to improve operational efficiency and prevent costly downtimes by identifying faults early. In order to classify healthy or faulty turbines both qualitatively and quantitatively, machine learning algorithms such as support vector machines and linear regression are used. The experimental results show that the performance measures are in good agreement with the results generated by the Support Vector Machine for the nonlinear models. The R squared value, which falls between 0 and 1, shows a better fit, while the Mean Absolute Percentage Error, which falls between 20 and 50%, indicates fair accuracy.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"247 ","pages":"Article 116852"},"PeriodicalIF":5.2,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A centroid contrastive multi-source domain adaptation method for fault diagnosis with category shift

IF 5.2 2区工程技术

Measurement Pub Date : 2025-01-25 DOI: 10.1016/j.measurement.2025.116801

Yufeng Li , Xinghan Xu , Lei Hu , Kai Sun , Min Han

引用次数: 0

Intelligent recognition and measurement for fatigue cracks in orthotropic steel decks: A comparative study of algorithms

IF 5.2 2区工程技术

Measurement Pub Date : 2025-01-25 DOI: 10.1016/j.measurement.2025.116867

Lexin Zhang , Zhiyu Jie , Zhong-Xian Li , Wei Lu , Hao Zheng , Wanzhen Wang

{"title":"Intelligent recognition and measurement for fatigue cracks in orthotropic steel decks: A comparative study of algorithms","authors":"Lexin Zhang , Zhiyu Jie , Zhong-Xian Li , Wei Lu , Hao Zheng , Wanzhen Wang","doi":"10.1016/j.measurement.2025.116867","DOIUrl":"10.1016/j.measurement.2025.116867","url":null,"abstract":"<div><div>Orthotropic steel bridge decks are highly susceptible to fatigue cracking under cyclic loading. To overcome the limitations in precision and efficiency of existing crack identification technologies, this study systematically analyzed the performance of four traditional machine learning algorithms, twelve deep learning algorithms, and forty-eight hybrid algorithms with various combinations in the task of fatigue crack image recognition. An innovative comprehensive evaluation method was proposed to assess these algorithms. It integrated an intelligent crack image recognition method with an automatic crack measurement technology to design an application. The results indicate that the combination of deep learning algorithms with K-Nearest Neighbors (KNN) or Support Vector Machines (SVM) algorithms significantly enhances recognition precision and efficiency. Among all the algorithms tested, the ResNet-101+SVM hybrid algorithm achieved the highest score of 98.88, with a final test set accuracy of 98.95 %, a transfer recognition rate of 95.97 %, and a training time of only 20.15 s, demonstrating excellent stability. Furthermore, it has successfully developed a low-cost and high-precision crack measurement technology, capable of controlling measurement errors within 2 %. It offers an efficient and reliable tool for intelligent detection and assessment of fatigue cracks in orthotropic steel bridge decks for engineers and researchers.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"247 ","pages":"Article 116867"},"PeriodicalIF":5.2,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-module fusion network for coal-rock interface recognition

IF 5.2 2区工程技术

Measurement Pub Date : 2025-01-25 DOI: 10.1016/j.measurement.2025.116861

Yunfen Qiao , Shujing Su , Weijie Qiao , Yuhong Gao

{"title":"A multi-module fusion network for coal-rock interface recognition","authors":"Yunfen Qiao , Shujing Su , Weijie Qiao , Yuhong Gao","doi":"10.1016/j.measurement.2025.116861","DOIUrl":"10.1016/j.measurement.2025.116861","url":null,"abstract":"<div><div>Accurately identifying the coal-rock interface is an effective path to enhance coal mining efficiency, guarantee coal quality, and reduce safety accidents. Due to the small differences in color and texture of coal rock, especially the complex texture, blurred boundary and irregular shape at the coal-rock junction, and the existence of interference factors such as low light and dust in the mine, the under-segmentation and mis-segmentation problem appear in the identification of coal-rock interface. In addition, the large number of parameters in the recognition model result in a low recognition efficiency. Therefore, this paper proposes a coal rock image segmentation network named CBAM-SP-CARAFE-DeepLabV3+ based on the deep learning technology. Firstly, the network selects MobileNetV2 as the backbone and embeds the Convolutional Block Attention Module (CBAM) into the Inverted Residual Block to achieve multi-dimensional extraction of coal and rock feature information; Secondly, the global average pooling in the Atrous Spatial Pyramid Pooling (ASPP) structure is replaced with Strip Pooling(SP), and SP is concatenated into the decoder to widen the receptive field and ensure the continuity of coal rock boundary information; Finally, the Content-Aware ReAssembly of FEatures(CARAFE) is used to up-sample the feature map to preserve coal and rock detail information. Experiments are conducted on the proposed network based on a self-made coal and rock dataset. The results suggest that the proposed network with fewer parameters has achieved a Pixel Accuracy (PA) of 95.45 % and a mean Intersection over Union (mIoU) of 81.32 %, which can achieve a better performance than some classical segmentation networks and some recent coal rock segmentation models.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"247 ","pages":"Article 116861"},"PeriodicalIF":5.2,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Total Partial Least Square Regression and its application in infrared spectra quantitative analysis

IF 5.2 2区工程技术

Measurement Pub Date : 2025-01-24 DOI: 10.1016/j.measurement.2025.116794

Mou Yi, Weizhen Chen, Jianguo Liu

引用次数: 0

Local deep learning of principal component regression model for spectroscopic calibration of time-varying spectra data

IF 5.2 2区工程技术

Measurement Pub Date : 2025-01-24 DOI: 10.1016/j.measurement.2025.116855

Junhua Zheng , Hansong Zhou , Xinyu Liu , Zeyu Yang , Zhiqiang Ge

{"title":"Local deep learning of principal component regression model for spectroscopic calibration of time-varying spectra data","authors":"Junhua Zheng , Hansong Zhou , Xinyu Liu , Zeyu Yang , Zhiqiang Ge","doi":"10.1016/j.measurement.2025.116855","DOIUrl":"10.1016/j.measurement.2025.116855","url":null,"abstract":"<div><div>The motivation of this paper is to improve the calibration performance of the widely used principal component regression model (PCR), aiming at time-varying spectra data. Inspired by the idea of layer-by-layer information extraction and processing in deep learning algorithms, the basic PCR model is firstly extended to the deep form by designing a layer-wise residual learning strategy. Then, a local deep learning framework is further formulated for calibration modeling of time-varying spectra data. Compared to traditional deep learning models, the lightweight deep PCR model has much lower computation burden and only several turning parameters, making it perfectly fitted to the local modeling framework. Besides, the engineering applicability of the simple PCR method can also be well reserved, such as easy implementation, transparent model structure, and superior interpretability of calibration results. Based on a detailed simulation case study on a public spectra dataset, both feasibility and effectiveness of the local deep learning model are confirmed.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"247 ","pages":"Article 116855"},"PeriodicalIF":5.2,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust radar wrist vital signs estimation exploiting phase correlation characteristics

IF 5.2 2区工程技术

Measurement Pub Date : 2025-01-24 DOI: 10.1016/j.measurement.2025.116792

Yibo Wang, Zhaocheng Yang, Ping Chu, Qifeng Lv, Jianhua Zhou

引用次数: 0

A tightly coupled integration of GNSS/IMU/LiDAR with parameterized semantic line and plane features to improve pose accuracy in complex environments

IF 5.2 2区工程技术

Measurement Pub Date : 2025-01-23 DOI: 10.1016/j.measurement.2025.116843

Junlong Cheng , Xiaohong Zhang , Feng Zhu , Jie Hu , Desheng Zhuo , Mohamed Freeshah

{"title":"A tightly coupled integration of GNSS/IMU/LiDAR with parameterized semantic line and plane features to improve pose accuracy in complex environments","authors":"Junlong Cheng , Xiaohong Zhang , Feng Zhu , Jie Hu , Desheng Zhuo , Mohamed Freeshah","doi":"10.1016/j.measurement.2025.116843","DOIUrl":"10.1016/j.measurement.2025.116843","url":null,"abstract":"<div><div>Continuous and accurate positioning is one of the critical requirements for established and emerging unmanned systems. Although the GNSS/IMU integration has become a widely-used navigation system, its performance is heavily dominated by GNSS. The dramatical accumulated error of IMU in GNSS outage and wrong updates results by GNSS outliers will influence the reliability of the integration system. In this work, we use light detection and ranging (LiDAR) to enhance the performance of the existing GNSS/IMU integration, where the raw measurements of three sensors are tightly integrated. The raw measurements of LiDAR are abstracted as parametric line and plane features. Two experiments are conducted to assess the proposed algorithm, and the results show that the addition of LiDAR significantly upgrades pose accuracy. In GNSS-challenge scenarios, LiDAR weakens the influence of GNSS outliers and improves the position accuracy by 77.6%, 67.4%, and 63.2% in the right, forward, and up directions, respectively.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"247 ","pages":"Article 116843"},"PeriodicalIF":5.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143168725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Concrete crack detection and severity assessment using deep learning and multispectral imagery analysis

IF 5.2 2区工程技术

Measurement Pub Date : 2025-01-23 DOI: 10.1016/j.measurement.2025.116825

Ching-Lung Fan

{"title":"Concrete crack detection and severity assessment using deep learning and multispectral imagery analysis","authors":"Ching-Lung Fan","doi":"10.1016/j.measurement.2025.116825","DOIUrl":"10.1016/j.measurement.2025.116825","url":null,"abstract":"<div><div>Traditional image-based crack detection methods often face limitations due to environmental noise, such as variable lighting, which impacts detection accuracy. This study presents an advanced approach that combines deep learning and spectral-index-based image analysis for concrete crack detection and severity assessment, addressing challenges in accuracy and stability. By employing the Single Shot Multibox Detector (SSD) trained on four types of spectral images—RGB, Global Environmental Monitoring Index (GEMI), Normalized Burn Ratio (NBR), and Normalized Difference Vegetation Index (NDVI)—the study demonstrates that detection accuracy significantly improves with multispectral imagery, especially for GEMI images, which achieved an average precision of 0.873. Regression analysis further reveals that crack orientation correlates more strongly with severity in transverse cracks than in longitudinal ones, providing critical information for automated maintenance strategies. A novel contribution of this work is integrating deep learning with multispectral data, representing a significant advancement in automated crack detection and offering enhanced detection precision and reliability in infrastructure health monitoring.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"247 ","pages":"Article 116825"},"PeriodicalIF":5.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169931","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sparse representation method based on propagation dispersion dictionary and its application in Terahertz non-destructive testing of polymethacrylimide foam

IF 5.2 2区工程技术

Measurement Pub Date : 2025-01-23 DOI: 10.1016/j.measurement.2025.116783

Yu Liu , Yefa Hu , Xinhua Guo , Jinguang Zhang , Guowu Zhang , Qiang Li , Jianhao Tian

{"title":"Sparse representation method based on propagation dispersion dictionary and its application in Terahertz non-destructive testing of polymethacrylimide foam","authors":"Yu Liu , Yefa Hu , Xinhua Guo , Jinguang Zhang , Guowu Zhang , Qiang Li , Jianhao Tian","doi":"10.1016/j.measurement.2025.116783","DOIUrl":"10.1016/j.measurement.2025.116783","url":null,"abstract":"<div><div>Terahertz (THz) non-destructive testing technology, as an emerging technique, can effectively identify internal defects in dielectric materials. However, during the longitudinal quantification of defects along the THz propagation direction, the material’s dispersion effect can impact the measurement accuracy of defect’s longitudinal size. To mitigate the impact of dispersion effects on the longitudinal positioning of defect profiles, in this paper, the over-complete dictionary of the traditional sparse representation (SR) method was optimized, and a SR method based on propagation dispersion dictionary (SRPDD) was proposed to conduct measurement of longitudinal dimensions of the void defects in the polymethacrylimide foam core of the fiber-reinforced composite foam sandwich structures. Through analysis of simulation signal and sample detection signal, the accuracy and stability of the SRPDD method in echo localization and longitudinal size quantification of defects are validated. This research optimizes the performance of THz non-destructive testing.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"247 ","pages":"Article 116783"},"PeriodicalIF":5.2,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143169936","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0