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Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions

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Friction 2024年第6期12卷 1272-1282页

作者： Florian KÖNIG Florian WIRSING Georg JACOBS Rui HE Zhigang TIAN Ming J.ZUO Institute for machine Elements and Systems engineering RWTH Aachen UniversityAachen 52062Germany department of Mechanical engineering University of AlbertaEdmonton T6G1H9Canada Qingdao International Academician Park Research Institute Qingdao 266000China

This study introduces a method to predict the remaining useful life(RUL)of plain bearings operating under stationary,wear-critical *** this method,the transient wear data of a coupled elastohydrodynamic lubrication(mixed-EHL)and wear simulation approach is used to parametrize a statistical,linear degradation *** method incorporates Bayesian inference to update the linear degradation model throughout the runtime and thereby consider the transient,system-dependent wear progression within the RUL prediction.A case study is used to show the suitability of the proposed *** results show that the method can be applied to three distinct types of post-wearing-in behavior:wearing-in with subsequent hydrodynamic,stationary wear,and progressive wear *** hydrodynamic operation leads to an infinite lifetime,the method is successfully applied to predict RUL in cases with stationary and progressive wear.

关键词： plain bearings wear modeling remaining useful life prediction Bayesian inference

Characterization of Electrical Tree Degradation of Epoxy Resin under Thermal and Temperature Stresses by Photoelastic Effect

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Chinese Journal of Electrical engineering 2024年第1期10卷 12-20页

作者： Hein Htet Aung Yuhuai Wang Jin Li Ying Zhang Tatsuo Takada School of Electrical and Information engineering Tianjin UniversityTianjin 300072China department of Power Generation and Transformation engineering Baoding Technical College of Electric PowerBaoding 071051China Measurement and Electric machine Control Laboratory Tokyo City UniversityTokyo 158-8557Japan

Epoxy resin is widely used in the support,insulation,and packaging components of electrical equipment owing to their excellent insulation,thermal,and mechanical ***,epoxy-resin insulation often suffers from thermal and mechanical stresses under extreme environmental conditions and a compact design,which can induce electrical tree degradation and insulation failure in electrical *** this study,the photoelastic method is employed to investigate the thermal-mechanical coupling stress dependence of the electrical treeing behavior of epoxy *** electrical tree growth morphology and stress distribution were observed using the photoelastic *** correlation between the tree length and overall accumulated damage with an increase in mechanical stress is *** results show that compressive stress retards the growth of electrical trees along the electric field,while tensile stress has accelerating *** proves that the presence of thermal stress can induce more severe accumulated damage.

关键词： Epoxy resin electrical tree degradation thermal stress mechanical stress photoelastic effects

A machine learning approach for the prediction of pore pressure using well log data of Hikurangi Tuaheni Zone of IODP Expedition 372,New Zealand

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Energy Geoscience

Energy Geoscience 2024年第2期5卷 225-231页

作者： Goutami Das Saumen Maiti machine Learning and Geocomputing Laboratory Department of Applied GeophysicsIIT(ISM)Dhanbad826004JharkhandIndia

Pore pressure(PP)information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field *** prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design,and mud weight estimation for wellbore ***,the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting,where sedimentation rate is relatively high and PP can be driven by various complex *** overcome these difficulties,an advanced machine learning(ML)tool is implemented in combination with empirical *** empirical method for PP prediction is comprised of data pre-processing and model establishment ***'s method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition ***-ray,sonic travel time,bulk density and sonic derived porosity are extracted from well log data for the theoretical framework *** normal compaction trend(NCT)curve analysis is used to check the optimum fitting of the low permeable zone *** statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model *** dataset is prepared and divided into two parts:Training and *** PP data and well log of borehole U1517A is pre-processed to scale in between[-1,+1]to fit into the input range of the non-linear activation/transfer function of the decision tree regression *** Decision Tree Regression(DTR)algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.

关键词： Well log Pore pressure machine learning(ML) IODP Hikurangi Tuaheni Zone IODP Expedition 372

Synergistic effect of adsorption and photocatalytic degradation of oilfield-produced water by electrospun photocatalytic fibers of Polystyrene/Nanorod-Graphitic carbon nitride

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Journal of Environmental Sciences 2024年第7期141卷 287-303页

作者： Parisa Azmoon Mehrdad Farhadian Alireza Pendashteh Amir H.Navarchian department of Chemical engineering Faculty of EngineeringUniversity of IsfahanIsfahanIran department of Chemical engineering Faculty of EngineeringUniversity of GuilanRashtIran department of Water and Environmental engineering Caspian Sea Basin Research CenterUniversity of GuilanRashtIran

Graphitic carbon nitride with nanorod structure(Nr-GCN)was synthesized using melamine as a precursor without any other reagents by hydrothermal pretreatment ***,FTIR,SEM,N_(2)adsorption-desorption from BET,UV-Vis DRS spectroscopy,and photoluminescence were used to characterize the prepared ***,the photoelectrochemical behavior of nanoparticles was studied by photocurrent transient response and cyclic voltammetry ***(PS)fibrous mat was fabricated by electrospinning technique and used as a support for the stabilization of the *** performance of the synthesized nanoparticles and photocatalytic fibers(PS/Nr-GCN)was evaluated in oilfield-produced water treatment under visible light *** this process,oil contaminants were adsorbed by hydrophobic polystyrene fibers and simultaneously degraded by *** removal efficiency of chemical oxygen demand(COD)has been obtained 96.6%and 98.4%by Nr-GCN and PS/Nr-GCN,respectively,at the optimum conditions of pH4,photocatalyst dosage 0.5 g/L,COD initial concentration 550 mg/L,and illumination time 150 *** gas chromatography-mass spectroscopy analysis results showed 99.3%removal of total petroleum hydrocarbons using photocatalytic fibers of PS/*** results demonstrated that the GCN has outstanding features like controllable morphology,visible-light-driven,and showing high potential in oily wastewater ***,the synergistic effect of adsorption and photocatalytic degradation is an effective technique in oilfield-produced water treatment.

关键词： Graphitic carbon nitride(GCN) Nanorod structure Polystyrene fibrous mat Photocatalytic fibers Oilfield-produced water(OPW)

Underwater inertial error rectification with limited acoustic observations

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Satellite Navigation 2024年第1期5卷 207-223页

作者： Hongqiong Tang Hongyang He Fangneng Li Jiangning Xu department of Navigation engineering Naval University of EngineeringWuhan430000China

Underwater inertial navigation is particularly difficult for the long-durance operations as many navigation systems such global satellite navigation systems are *** acoustic signal is a marvelous choice for underwater inertial error rectification due to its underwater penetration ***,the traditional Acoustic Positioning Systems(APS)are expensive and incapable of positioning with limited acoustic *** novel underwater inertial error rectification algorithms with limited acoustic observations are *** first one is the single acoustic-beacon Range-only Matching Aided Navigation(RMAN)method,which is inspired by matching navigation without reference maps and presented for the first *** second is the improved single acoustic-beacon Virtual Long Baseline(VLBL)method,which considers the impact of indicated relative position increments on virtual beacon *** RMAN and improved VLBL are further developed when multi acoustic-beacons are available,named mAB-RMAN and *** comprehensive simulations and field investigations were *** results demonstrated that the proposed methods achieved excellent accuracy and stability compared to the baseline,specifically,the mAB-RMAN and mAB-VLBL can reduce the inertial error by more than 90%and 98%when using single and double acoustic-beacons,*** proposed techniques will provide new perspectives for underwater positioning,navigation,and timing.

关键词： Underwater PNT Range-only matching aided navigation Virtual long baseline Inertial error rectification

Artificial Intelligence Prediction of One-Part Geopolymer Compressive Strength for Sustainable Concrete

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Computer Modeling in engineering & Sciences 2024年第10期141卷 525-543页

作者： Mohamed Abdel-Mongy Mudassir Iqbal M.Farag Ahmed.M.Yosri Fahad Alsharari Saif Eldeen A.S.Yousef department of Civil engineering College of EngineeringJouf UniversitySakaka72388Saudi Arabia department of Civil engineering University of Engineering and TechnologyPeshawar54890Pakistan Civil engineering department Faculty of EngineeringAl-Azhar UniversityCairo11884Egypt Civil engineering department Faculty of EngineeringAswan UniversityAswan81528Egypt

Alkali-activated materials/geopolymer(AAMs),due to their low carbon emission content,have been the focus of recent studies on ecological *** terms of performance,fly ash and slag are preferredmaterials for precursors for developing a one-part ***,determining the optimum content of the input parameters to obtain adequate performance is quite challenging and scarcely ***,in this study,machine learning methods such as artificial neural networks(ANN)and gene expression programming(GEP)models were developed usingMATLAB and GeneXprotools,respectively,for the prediction of compressive strength under variable input materials and content for fly ash and slag-based one-part *** database for this study contains 171 points extracted from literature with input parameters:fly ash concentration,slag content,calcium hydroxide content,sodium oxide dose,water binder ratio,and curing *** performance of the two models was evaluated under various statistical indices,namely correlation coefficient(R),mean absolute error(MAE),and rootmean square error(RMSE).In terms of the strength prediction efficacy of a one-part geopolymer,ANN outperformed *** and parametric analysis were also performed to identify the significant contributor to *** to a sensitivity analysis,the activator and slag contents had the most effects on the compressive strength at 28 *** water binder ratio was shown to be directly connected to activator percentage,slag percentage,and calcium hydroxide percentage and inversely related to compressive strength at 28 days and curing temperature.

关键词： Artificial intelligence techniques one-part geopolymer artificial neural network gene expression modelling sustainable construction polymers

ASLP-DL—A Novel Approach Employing Lightweight Deep Learning Framework for Optimizing Accident Severity Level Prediction

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Computers, Materials & Continua 2024年第2期78卷 2535-2555页

作者： Saba Awan Zahid Mehmood department of Software engineering University of Engineering and TechnologyTaxila47050Pakistan department of Computer engineering University of Engineering and TechnologyTaxila47050Pakistan

Highway safety researchers focus on crash injury severity,utilizing deep learning—specifically,deep neural networks(DNN),deep convolutional neural networks(D-CNN),and deep recurrent neural networks(D-RNN)—as the preferred method for modeling accident *** learning’s strength lies in handling intricate relation-ships within extensive datasets,making it popular for accident severity level(ASL)prediction and *** prior success,there is a need for an efficient system recognizing ASL in diverse road *** address this,we present an innovative Accident Severity Level Prediction Deep Learning(ASLP-DL)framework,incorporating DNN,D-CNN,and D-RNN models fine-tuned through iterative hyperparameter selection with Stochastic Gradient *** framework optimizes hidden layers and integrates data augmentation,Gaussian noise,and dropout regularization for improved *** and factor contribution analyses identify influential *** on three diverse crash record databases—NCDB 2018–2019,UK 2015–2020,and US 2016–2021—the D-RNN model excels with an ACC score of 89.0281%,a Roc Area of 0.751,an F-estimate of 0.941,and a Kappa score of 0.0629 over the NCDB *** proposed framework consistently outperforms traditional methods,existing machine learning,and deep learning techniques.

关键词： Injury severity prediction deep learning feature

Statistical analysis and interpretation on lightning impulse breakdown voltages of ester liquids under varying field uniformity

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High Voltage 2024年第1期9卷 22-34页

作者： Haichuan Yu Qiang Liu Zhongdong Wang department of Electrical and Electronic engineering The University of ManchesterManchesterUK department of engineering University of ExeterExeterUK

Ester liquids(ELs),as the alternative transformer liquids to the conventional mineral oils(MOs),have been increasingly used in power transformers with high voltage *** various properties,the lightning impulse breakdown voltages(LIBDVs)have been studied elaborately in the past two decades under different electric field *** electric field uniformities do have an effect on the breakdown characteristics,for example,breakdown mechanisms,polarity effects,and differences of LIBDV between ELs and MOs,which is quantitatively investigated based on the LIBDVs collected from the *** breakdown mechanism transition phenomenon between streamer initiation dominated breakdown and streamer propagation dominated breakdown has been reviewed ***,the transition field factor boundaries to observe the polarity effect on LIBDVs are taken at around 20 for ELs and 10 for ***,the transition field factor boundaries to observe the LIBDV distinctions between ELs and MOs are about 109 and 14 under positive and negative lightning impulse,*** systematic analysis and interpretation on the transition field factor boundaries among different phenomena can provide insights on insulation design of ELs filled power transformers.

关键词： field lightning breakdown

Formation adaptation in obstacle-cluttered environments via MPC-based trajectory planning

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Science China(Information Sciences) 2024年第7期67卷 321-323页

作者： Yuda CHEN Zhongkui LI department of Mechanics and engineering Science College of Engineering Peking University

Formation is a typical collective behavior in multiagent systems utilized in areas such as inspection, surveillance, and transportation. Commonly-used approaches to achieve formation include virtual structure [1], con...

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