Measurement Science and Technology最新文献

{"title":"Investigation of Operational Settings, Environmental Conditions, and Faults on the Gas Turbine Performance","authors":"Shazaib Ahsan, T. Lemma, Muhammad Baqir Hashmi, Xihui Liang","doi":"10.1088/1361-6501/ad678c","DOIUrl":"https://doi.org/10.1088/1361-6501/ad678c","url":null,"abstract":"\u0000 Gas turbine engines are complex mechanical marvels widely employed in diverse applications such as marine vessels, aircraft, power generation, and pumping facilities. However, their intricate nature renders them susceptible to numerous operational faults, significantly compromising their performance and leading to excessive emissions, consequently incurring stringent penalties from environmental regulatory bodies. Moreover, the deterioration of gas turbine performance is exacerbated by variations in working conditions based on operational settings and environmental conditions. Past studies have focused on certain working conditions that limit effectiveness in real-world applications where operational settings and environmental conditions vary during operations. The influence of these working conditions on the performance of gas turbines also needs to be assessed, as they can lead to different fault patterns resulting in unplanned maintenance, unnecessary maintenance costs, unsafe conditions and stringent penalties. This study uses the Gas Turbine Simulation Program (GSP) to simulate a high-bypass turbofan engine, analyzing the combined effects of operational settings and environmental conditions on engine performance while also incorporating simulations of common gas turbine faults like fouling and erosion in various locations and severities along the gas path. The model's accuracy is confirmed by low Mean Absolute Percentage Errors (MAPE) of 0.004% of thrust at the cycle reference point and 0.15% and 0.28% at 2 km and 7 km altitudes, respectively, demonstrating the model's robustness across varying operational scenarios. In conclusion, this research highlights the significant effects of operational settings and environmental factors on gas turbine performance, particularly impacting specific fuel consumption and thrust. Our findings indicate substantial impacts of operational settings and environmental factors on fuel consumption and thrust. Specifically, compressor fouling and low-pressure turbine erosion increase Nitrogen Oxide (NOx) emissions by 4.5% and 11.1%, while fouling of nozzle guide vanes and high-pressure turbine erosion raise unburnt hydrocarbon (UHC) by 10.0% and 20.2%, and carbon monoxide (CO) by 3.2% and 5.2%, respectively, compared to a healthy engine. These insights highlight the importance of component-specific degradation in influencing gas turbine performance and emissions.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"55 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141804541","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}

{"title":"A contour detection method for bulk material piles based on cross-source point cloud registration","authors":"Pingjun Zhang, Hao Zhao, Guangyang Li, Xipeng Lin","doi":"10.1088/1361-6501/ad678b","DOIUrl":"https://doi.org/10.1088/1361-6501/ad678b","url":null,"abstract":"\u0000 In the field of automatic bulk material loading, accurate detection of the profile of the material pile in the compartment can control its height and distribution, thus improving the loading efficiency and stability, therefore, this paper proposes a new method for pile detection based on cross-source point cloud registration. First, 3D point cloud data are simultaneously collected using lidar and binocular camera. Second, feature points are extracted and described based on 3D scale-invariant features (3DSIFT) and 3D shape contexts (3DSC) algorithms, and then feature points are used in progressive sample consensus (PROSAC) algorithm to complete coarse matching. Then, bi-directional KD-tree accelerated iterative closest point (ICP) is established to complete the fine registration. Ultimately, the detection of the pile contour is realized by extracting the point cloud boundary after the registration. The experimental results show that the registration errors of this method are reduced by 54.2%, 52.4%, and 14.9% compared with the other three algorithms, and the relative error of the pile contour detection is less than 0.2%.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"19 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803631","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}

{"title":"Sub-Macroscopic Inclusion Classification in Bearing Steels Based on LFCN and Ultrasonic Testing","authors":"Ningqing Zhang, Xiongbing Chen, Yizhen Wang","doi":"10.1088/1361-6501/ad6788","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6788","url":null,"abstract":"\u0000 With the improvement of science and technology, the demand for advanced steel with excellent performance has gradually increased. Therefore, the evaluation of steel internal cleanness is an important indicator for the evaluation of material quality. Sub-macroscopic inclusions, which size from 50μm to 400μm and cannot be detected under the domestic and international bearing steel testing standard, are bound to affect the quality, stability and service life of bearing steel seriously. Hence, the researches of inclusion control technology has gradually attracted attention in the academia and industrial manufacture field. In this paper, we propose an end-to-end Long Short-term Memory Fully Convolutional Network (LFCN) classification model, and verify the effectiveness on the large-scale sub-macroscopic inclusion signal data set collected by ultrasonic experiments. To the best of our knowledge, this study is the first one in this field that has acquire such large amount of experimental sub-macroscopic signal data and solve the classification task by FCN. Especially, our framework can accurately detect the features of sub-macroscopic inclusions, which meets the urgent need of the metallurgical industry. The accuracy rate of proposed model is 88.97%, which is state-of-the-art experimental result among other strong time series classifiers.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"32 23","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141802785","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}

{"title":"The algorithm for denoising point clouds of annular forgings based on Grassmann manifold and density clustering","authors":"Yucun Zhang, 安 王, Tao Kong, Xianbin Fu, Dongqing Fang","doi":"10.1088/1361-6501/ad66f0","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66f0","url":null,"abstract":"\u0000 In the industrial sector, annular forgings serve as critical load-bearing components in mechanical equipment. During the production process, the precise measurement of the dimensional parameters of annular forgings is of paramount importance to ensure their quality and safety. However, owing to the influence of the measurement environment, the manufacturing process of annular forgings can introduce varying degrees of noise, resulting in inaccurate dimensional measurements. Therefore, researching methods for three-dimensional point cloud data to eliminate noise in annular forging point clouds is of significant importance for improving the accuracy of forging measurements. This paper presents a denoising approach for three-dimensional point cloud data of annular forgings based on Grassmann manifold and density clustering (GDAD). First, within the Grassmann manifold, the core points for density clustering are determined using density parameters. Second, density clustering is performed within the Grassmann manifold, with the Cauchy distance replacing the Euclidean distance to reduce the impact of noise and outliers on the analysis results. Finally, a search tree model was constructed to filter out incorrect point cloud clusters. The fusion of clustering results and the search tree model achieved denoising of point cloud data. Simulation experiments on annular forgings demonstrate that GDAD effectively eliminates edge noise in annular forgings and performs well in denoising point-cloud models with varying levels of noise intensity","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"3 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807470","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}

{"title":"Autonomous ground vehicle gravity anomaly measurement and dynamic error compensation","authors":"Xinyu Li, Zhaofa Zhou, Zhili Zhang, Zhenjun Chang, Shiwen Hao, Hui Duan","doi":"10.1088/1361-6501/ad6702","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6702","url":null,"abstract":"\u0000 To address the issue that dynamic gravity anomaly measurement is overly dependent on GNSS and can’t be measured autonomously at this stage, this paper proposes an autonomous ground vehicle dynamic gravity anomaly measurement method based on a strapdown inertial navigation system (SINS), odometer (OD), barometer and platform gravimeter. The SINS/OD /barometer integrated navigation solution delivers high-precision navigation parameters, completes the calculation of correction terms, and performs the autonomous dynamic gravity anomaly measurement combined with the primary measurement results of the platform gravimeter. Numerical calculations provide the requirements for the application of the proposed method, and the cut-off frequency for extracting gravity anomalies is 0.02 Hz, as determined by power spectral density analysis. In order to further improve the measurement accuracy and account for dynamic errors caused by vehicle maneuvering, a long-short-term memory (LSTM) model of recurrent neural network (RNN) is introduced. A series of experiments under multiple circumstances with repeated lines were conducted in Tianjin, China, and the static measurements along the line were taken using CG-5 to provide true values of gravity anomalies. The results demonstrate that the autonomous measurement scheme can achieve accuracy comparable to GNSS-assisted, and that dynamic error compensation algorithm based on LSTM improves the dynamic gravity measurements accuracy significantly without sacrificing the spatial resolution of gravity anomalies.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807348","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}

{"title":"Visual Localization and Quantitative Detection Method for Thermal Defects in Cable Terminals of High-Speed Trains Based on a Temperature Derivative","authors":"Kai Liu, Shibo Jiao, Guangbo Nie, Bo Gao, Zhixiang Yang, Donli Xing, Guangning Wu","doi":"10.1088/1361-6501/ad6701","DOIUrl":"https://doi.org/10.1088/1361-6501/ad6701","url":null,"abstract":"\u0000 Cable terminal defect detection plays an important role in ensuring the safe and stable operation of high-speed trains (HST). In this paper, a numerical model of the electromagnetic thermal field of overheating defects of cable terminal shielding grids and a method of detecting internal defects of cable terminals - even-order temperature derivative (EOTD) is proposed for quantitative detection of internal defective structures of vehicle-mounted cable terminals. Firstly, a numerical model of the electromagnetic thermal field of cable terminals under the condition of leakage current is constructed, through which the temperature field distribution characteristics of different defective structures are analyzed. Then, the intuitive location of the defective region is obtained by investigating the derivative characteristics of the temperature image, and the depth and intensity of the defects are quantitatively assessed by using the main side peak (MSP) distances and the MSP values extracted from the derivative curves. Finally, the simulation and experimental results achieve the identification of defect structures and the quantitative detection of defect depth and intensity, proving the effectiveness and accuracy of the proposed method.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"65 50","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806507","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}

{"title":"Noncontact measurement of rectangular splines shaft based on line-structured light","authors":"Siyuan Liu, Honglin Li, Conghui Wang, Fenghui Lian, J. Miao, Zhengyi Hu","doi":"10.1088/1361-6501/ad66fd","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66fd","url":null,"abstract":"\u0000 The rectangular spline shaft, a typical type of shaft-tooth component, plays a significant role in mechanical transmissions. Existing methods for detecting size and positional tolerance in spline shafts often rely on contact-based measurement tech-niques, including specialized gauges and coordinate measuring machines (CMMs). To enhance the measurement efficiency, this paper proposes a method for measuring based on line-structured light. Firstly, a classification algorithm for data points on large and small cylindrical surfaces and keyway surface of spline shaft is established, contributing to the automatic measurement. Secondly, a coaxiality error measurement model is established based on the overall least squares method, improving the measurement accuracy. Finally, a measurement model for key width and positional tolerance is established through the rotation of the spline axis. In experiments, the size and positional tolerance of the spline shaft obtained using this method are compared with measurements by CMM, meeting the general machining accuracy requirements.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"31 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808152","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}

{"title":"Fault diagnosis method and experimental research of reciprocating seal based on CFD-GAN-AE","authors":"Yi Zhang, Ling Hu, Wei He","doi":"10.1088/1361-6501/ad66fc","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66fc","url":null,"abstract":"\u0000 Hydraulic impactors are crucial for oil and gas exploration, but seal failure is a common issue, having an effective technique for diagnosing sealing faults can provide dependable operational and maintenance assistance for Hydraulic impactors. However, identifying wear failures is challenging and there is limited data available, there has been significant interest in intelligent defect diagnosis technology that is based on deep learning in recent years. Therefore, we propose a method to enhance the data and identify faults through deep learning. Initially, the CFD method was used to simulate seal leakage and determine whether factors such as pressure can indicate varying levels of leaking in the seal, this approach provides a theoretical foundation for signal gathering experiments. Next, the EMD approach is used to separate the non-smooth pressure signal from the seal experiment, revealing fault features that indicate the extent of leakage. Finally, the improved GAN method is suggested to balance imbalanced samples by utilizing the sample overlap rate, it is paired with the AE algorithm to categorize different levels of leakage. Furthermore, a comparative analysis is conducted between the proposed methodology and several classical fault diagnosis methods. This work investigates seal damage through the lens of computational fluid dynamics and the fault identification of uneven seal samples is accomplished.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"5 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141808691","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}

{"title":"Methods and research for deformation monitoring of earth and rock dams based on close-range photogrammetry","authors":"Kui Wang, Yang Cheng, Yong Xiong, Qiuqi Wang, M. Zhao","doi":"10.1088/1361-6501/ad66f6","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66f6","url":null,"abstract":"\u0000 Traditional means of monitoring deformation in earth and rock dams encounter challenges such as low monitoring efficiency and limited coverage. Despite the potential of emerging technologies such as GPS and three-dimensional laser scanning, their adoption is expensive and hard to promote. This paper presents a deformation monitoring method for earth and rock dams based on the close-range photogrammetry technique. The proposed approach focuses on analytical algorithm the design and deployment of monitoring points, photographic schemes, camera checking and calibration, as well as deformation analysis methods. Initially, based on the analysis of the parsing algorithms’ applicability, they are fused to address the shortcomings of common image parsing methods in meeting the requirements of high precision and multi-image processing for deformation monitoring of earth and rock dams. Subsequently, the fused algorithm is introduced to analyze the acquired image data for 3D reconstruction, and the deformation in earth and rock dams is assessed based on the generated dense point cloud model. The proposed deformation monitoring method is applied to Pine Bridge Reservoir Dam, and the results demonstrated its capacity to comprehensively analyze the deformation. Furthermore, the required equipment is simple and easy to operate, aligning with the requirements for deformation monitoring accuracy of earth and rock dams.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"69 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806675","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}

{"title":"A self-supervised learning method for fault detection of wind turbines","authors":"Shaodan Zhi, Haikuo Shen","doi":"10.1088/1361-6501/ad66f2","DOIUrl":"https://doi.org/10.1088/1361-6501/ad66f2","url":null,"abstract":"\u0000 As promising solutions to condition-based maintenance of wind turbines, artificial intelligence-based techniques have drawn extensive attention in the era of industry 4.0. However, accurate fault detection is still challenging owing to volatile operating conditions in real-world settings. To handle this problem, a novel method is proposed for fault detection of wind turbines. Specifically, a data augmentation scheme is developed to simulate the effects of time-varying environments and noise. Then, a self-supervised proxy task of variant prediction is designed and conducted. In this way, valid data representations can be extracted to represent the health status of wind turbines. Additionally, the compactness of data representations is guaranteed by the directional evolution, which can relieve the confusion of health conditions. The effectiveness of the proposed method is verified with actual measurements. Using the proposed method, several faults can be detected more than 10 days earlier, and blade breakage can be identified more than 22 hours earlier. Furthermore, the developed method outperforms several benchmark approaches.","PeriodicalId":510602,"journal":{"name":"Measurement Science and Technology","volume":"13 7","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809475","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}