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Intelligent fault diagnosis methods toward gas turbine: A review

Chinese Journal of Aeronautics 2024年第4期37卷 93-120页

作者： Xiaofeng LIU Yingjie CHEN Liuqi XIONG Jianhua WANG Chenshuang LUO Liming ZHANG Kehuan WANG School of transportation Science and Engineering Beihang UniversityBeijing 100191China systems Engineering Research institute China State Shipbuilding CorporationBeijing 100094China

Fault diagnosis plays a significant role in conducting condition-based maintenance and health management for gas turbines(GTs) to improve reliability and reduce costs. Various diagnosis methods developed by modeling engine systems or certain components implement faults detection and diagnosis based on the measurement of systemic parameters deviations. However, these conventional model-based methods are hindered by limitations of inability to handle the nonlinear nature, measurement uncertainty, fault coupling and other implementing problems. Recently, the development of artificial intelligence algorithms has provided an effective solution to the above problems, triggering broad researches for data-driven fault diagnosis methods with better accuracy,dynamic performance, and universality. This paper presents a systematic review of recently proposed intelligent fault diagnosis methods for GT engines, according to the classification of shallow learning methods, deep learning methods and hybrid intelligent methods. Moreover, the principle of typical algorithms, the evolution of enhanced methods, and the assessment of pros and cons are summarized to conclude the present status and look forward to the future in the field of GT fault diagnosis. Possible directions for development in method validation, information fusion, and interpretability of intelligent diagnosis methods are concluded in the end to provide insightful concepts for scholars in related fields.

关键词： Fault diagnosis Health management Gas turbine Artificial intelligence Intelligent diagnosis method

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Research on the stability of virtual coupling train formation based on a PID controller under the influence of time delay

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transportation Safety and Environment 2024年第3期6卷 133-139页

作者： Sixuan Qiu Ningzhou Li Xiaojuan Wei Gaosong Li School of Railway transportation Shanghai Institute of TechnologyShanghai 201418China

As the latest research direction of train-to-train communication,virtual coupling train formation technology has attracted the attention of many *** paper studies the influence of time delay on the formation of virtual coupled *** proposed a distributed PID controller,which means that trains can still form a stable fleet operation under the influence of communication delay and control *** modelling and analysis,the research uses Matlab to conduct simulation,involving two sets of *** including speed,acceleration,position,position error,expected distance between adjacent trains and actual distance between adjacent trains are *** results demonstrate that the distributed PID controller can effectively control the impact of time delay on the virtual coupling fleet.

关键词： virtual coupling train-to-train communication time delay PID controller

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang the State Key Laboratory for Management and Control of Complex systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 IEEE the transportation and Autonomous systems institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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Removal of Cr(Ⅵ)by glutaraldehyde-crosslinked chitosan encapsulating microscale zero-valent iron:Synthesis,mechanism,and longevity

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Journal of Environmental Sciences 2024年第8期142卷 115-128页

作者： Yijun Duan Fang Liu Xiang Liu Miao Li School of Environment Tsinghua UniversityBeijing 100084China transportation institute of Inner Mongolia University Hohhot 010070China

Microscale zero-valent iron(mZVI)has shown great potential for groundwater Cr(Ⅵ)***,low Cr(Ⅵ)removal capacity caused by passivation restricted the wide use of *** prepared mZVI/GCS by encapsulating mZVI in a porous glutaraldehydecrosslinked chitosan matrix,and the formation of the passivation layer was alleviated by reducing the contact between zero-valent iron *** average pore diameter of mZVI/GCS was 8.775 nm,which confirmed the mesoporous characteristic of this *** of batch experiments demonstrated that mZVI/GCS exhibited high Cr(Ⅵ)removal efficiency in a wide range of pH(2-10)and temperature(5-35℃).Common groundwater coexisting ions slightly affected m ZVI/*** material showed great reusability,and the average Cr(Ⅵ)removal efficiency was 90.41%during eight *** this study,we also conducted kinetics and isotherms ***-second-order model was the most matched kinetics *** Cr(Ⅵ)adsorption process was fitted by both Langmuir and Freundlich isotherms models,and the maximum Langmuir adsorption capacity of m ZVI/GCS reached243.63 mg/g,which is higher than the adsorption capacities of materials reported in most of the previous ***,the column capacity for Cr(Ⅵ)removal of a mZVI/GCS-packed column was 6.4 times higher than that of a mZVI-packed column in a 50-day ***,mZVI/GCS with a porous structure effectively relieved passivation problems of mZVI and showed practical application prospects as groundwater Cr(Ⅵ)remediation material with practical application prospects.

关键词： Chitosan Glutaraldehyde mZVI Cr(Ⅵ) Groundwater

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Bending Failure Mode and Prediction Method of the Compressive Strain Capacity of A Submarine Pipeline with Dent Defects

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China Ocean Engineering 2024年第4期38卷 636-647页

作者： HOU Fu-heng JIA Lu-sheng CHEN Yan-fei ZHANG Qi ZHONG Rong-feng WANG Chun-sha School of Mechanical and transportation Engineering China University of PetroleumBeijingBeijing 102249China CNOOC Research institute Ltd.Beijing 100028 China

A dent is a common type of defects for submarine *** submarine pipelines,high hydrostatic pressure and internal pressure are the main *** pipelines bend due to complex subsea conditions,the compression strain capacity may be *** into the local buckling failure and accurate prediction of the compressive strain capacity are important.A finite element model of a pipeline with a dent is *** buckling failure under a bending moment is investigated,and the compressive strain capacity is *** effects of different parameters on pipeline local buckling are *** results show that the dent depth,external pressure and internal pressure lead to different local buckling failure modes of the pipeline.A higher internal pressure indicates a larger compressive strain capacity,and the opposite is true for external *** the ratio of external pressure to collapse pressure of intact pipeline is greater than 0.1,the deeper the dent,the greater the compressive strain capacity of the *** as the ratio is less than 0.1,the opposite is *** the basis of these results,a regression equation for predicting the compressive strain capacity of a dented submarine pipeline is proposed,which can be referred to during the integrity assessment of a submarine pipeline.

关键词： submarine pipeline dent defect bending load local buckling compressive strain capacity

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Interface failure of segmental tunnel lining strengthened with steel plates based on fracture mechanics

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Frontiers of Structural and Civil Engineering 2024年第1期18卷 137-149页

作者： Yazhen SUN Yang YU Jinchang WANG Longyan WANG School of transportation and Geomatics Engineering Shenyang Jianzhu UniversityShenyang 110168China School of Civil Engineering Shenyang Jianzhu UniversityShenyang 110168China institute of transportation Engineering Zhejiang UniversityHangzhou 310058China Center for Balance Architecture Zhejiang UniversityHangzhou310058China

Segmental tunnel lining strengthened with steel plates is widely used worldwide to provide a permanent strengthening *** existing studies assume an ideal steel-concrete interface,ignoring discontinuous deformation characteristics,making it difficult to accurately analyze the strengthened structure’s failure *** this study,interfacial fracture mechanics of composite material was applied to the segmental tunnel lining strengthened with steel plates,and a numerical three-dimensional solid nonlinear model of the lining structure was established,combining the extended finite element method with a cohesive-zone model to account for the discontinuous deformation characteristics of the *** results accurately describe the crack propagation process,and are verified by full-scale ***,dynamic simulations based on the calibrated model were conducted to analyze the sliding failure and cracking of the steel-concrete ***,detailed location of the interface bonding failure are further verified by model *** results show that,the cracking failure and bond failure of the interface are the decisive factors determining the instability and failure of the strengthened *** proposed numerical analysis is a major step forward in revealing the interface failure mechanism of strengthened composite material structures.

关键词： segmental tunnel lining steel plate strengthening connecting interface cohesive-zone model extended finite element method

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Short-term prediction of on-street parking occupancy using multivariate variable based on deep learning

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Journal of Traffic and transportation Engineering(English Edition) 2024年第1期11卷 28-40页

作者： Mengqi Lyu Yanjie Ji Chenchen Kuai Shuichao Zhang School of transportation Southeast UniversityNanjing 211189China Shandong Provincial Communications Planning and Design institute Group Co. Ltd.Jinan 250101China School of Civil and transportation Engineering Ningbo University of TechnologyNingbo 315211China

Short-term prediction of on-street parking occupancy is essential to the ITS system,which can guide drivers in finding vacant parking *** the spatial dependencies and exogenous dependencies need to be considered simultaneously,which makes short-term prediction of on-street parking occupancy ***,this paper proposes a deep learning model for predicting block-level parking ***,the importance of multiple points of interest(POI)in different buffers is sorted by Boruta,used for feature *** results show that different types of POI data should consider different buffer *** based on the real on-street parking data,long short-term memory(LSTM)that can address the time dependencies is applied to predict the parking *** results demonstrate that LSTM considering POI data after Boruta selection(LSTM(+BORUTA))outperforms other baseline methods,including LSTM,with an average testing MAPE of 11.78%.The selection process of POI data helps LSTM reduce training time and slightly improve the prediction performance,which indicates that complex correlations among the same type of POI data in different buffer zones will also affect the prediction accuracy of *** there are more restaurants on both sides of the street,the prediction performance of LSTM(+BORUTA)is significantly better than that of LSTM.

关键词： Smart parking Parking occupancy Short-term prediction Long short-term memory Boruta

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Effects of Atmospheric Pressure on Developmental Characteristics and the Stability of air Entraining Agent for Concrete

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Journal of Wuhan University of Technology(Materials Science) 2024年第4期39卷 979-989页

作者：李立辉牛开民 TIAN Bo CHEN Xin FU Ziqian ZHU Xuwei School of Civil Engineering Chongqing Jiaotong UniversityChongqing 400074China transportation Science and Engineering Harbin Institute of TechnologyHarbin 150090China Research institute of Highway Ministry of TransportBeijing 100088China

In order to ascertain the effects of atmospheric pressure on developmental characteristics and the stability of AEA(air-entraining agent)solution bubbles,AEA solution experiments and AEA solution bubble experiments were,respectively,conducted in Peking(50 m,101.2 kPa)and Lhasa(3,650 m,63.1 kPa).Surface tensions and inflection-point concentrations were tested based on AEA solutions,whilst developmental characteristics,thicknesses and elastic coefficients of liquid films were tested based on air bubbles of AEA *** study involved three types of AEAs,which were TM-O,226A,and *** experimental results show that initial sizes of TM-O,226A,and 226S are,respectively,increased by 43.5%,17.5%,and 3.8%.With the decrease of ambient pressure,the drainage rate and the drainage index of AEA solution bubbles *** experiments show that the liquid film thicknesses of all tested AEA solution bubbles are in micron *** the atmospheric pressure decreases from 101.2 to 63.1 kPa,the liquid film thicknesses of three types of AEA solutions decrease in various degrees;and film elasticities at critical thicknesses *** film of 226S solution bubbles is the most stable,presenting as a minimum thickness *** should be noted that elastic coefficient of liquid film only represents the level at critical thickness,thus it can not be applied as the only evaluating indicator of bubble *** a type of AEA,factors affecting the stability of its bubbles under low atmospheric pressure include initial bubbles size,liquid film thickness,liquid film elasticity,ambient temperature,etc.

关键词： air-entraining agent surface tension bubble size liquid film thickness liquid film elasticity atmospheric pressure

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A Combination Prediction Model for Short Term Travel Demand of Urban Taxi

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Computers, Materials & Continua 2024年第6期79卷 3877-3896页

作者： Mingyuan Li Yuanli Gu Qingqiao Geng Hongru Yu School of Traffic and transportation Beijing Jiaotong UniversityBeijing100044China Transport Planning and Research institute Ministry of TransportBeijing100028China

This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing *** model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel *** CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and *** on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction *** this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban *** combined prediction model is tested on a taxi travel demand dataset in a certain area of *** results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 ***,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed *** results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.

关键词： Urban transport taxi travel demand prediction CEEMDAN-ConvLSTM modal components

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Literature Review of Driving Fatigue Research based on Bibliometric Analysis

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Journal of Traffic and transportation Engineering(English Edition) 2024年

作者： Fengxiang Guo Yiwen Zhou Xiao'an Wang Jing Cai Faculty of transportation Engineering， Kunming University of Science and Technology Yunnan transportation Science Research institute Co.， Ltd.

Driving for a long time can lead to fatigue， which can affect information processing and decision-making， potentially threatening driving safety. By exploring the generation and variation patterns of driving fatigue and clarifying the application status of its detection and warning technology， traffic safety can be improved. In order to understand the research progress of driving fatigue， based on the Web of Science core database， this paper obtained 2127 English literature published from 1998 to 2023 (as of May 12， 2023)， covering a total of 5963 authors and 6019 keywords， and the bibliometric tool VOSviewer and R-Bibliometrix are used to analyze the literatures. Firstly， the literature statistical analysis is carried out from annual distribution， country distribution， source journals， and core authors. Secondly， the research hotspots and trends are analyzed from keywords co-occurrence， high-impact literature， theme evolution， and development trends. Finally， the development trend of driving fatigue research is discussed. The result shows that the hotspots in the field of driving fatigue include driving fatigue detection， fatigue driving risk and fatigue research of professional drivers， driver alertness and coping strategies for driver fatigue， and driver fatigue under automatic driving. And the improvement of the accuracy and reliability of driving fatigue detection will be the difficult point to break through in the future. It is necessary to build a multi-disciplinary and meticulously quantified driving fatigue and its risk assessment system， and strengthen the control of fatigue risks. Research on intervention measures and education training for professional drivers needs to be deepened， while exploring the acceptance and compliance of intervention measures. Moreover， the evolution law and prevention strategies of driver fatigue under automatic conditions should be the focus of future research.

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