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An Estimate Method of Parametric in Reliability Engineering

Han Ming

Strategic Study of CAE 2003, Volume 5, Issue 3,   Pages 51-56

Abstract:

In this paper, the Bayesian method, an estimate method for parameter in reliability engineering isThe author gives definition of the new Bayesian estimate for failure probability and failure rate, andshows the estimate of the failure probability and the failure rate by new Bayesian method.

Keywords： reliability engineering     parameter estimate     new Bayesian estimate     failure probability    

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Anovel approach of noise statistics estimate using H∞ filter in target tracking

Xie WANG,Mei-qin LIU,Zhen FAN,Sen-lin ZHANG

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 5,   Pages 449-457 doi: 10.1631/FITEE.1500262

Abstract: Noise statistics are essential for estimation performance. In practical situations, however, a priori information of noise statistics is often imperfect. Previous work on noise statistics identification in linear systems still requires initial prior knowledge of the noise. A novel approach is presented in this paper to solve this paradox. First, we apply the H∞ filter to obtain the system state estimates without the common assumptions about the noise in conventional adaptive filters. Then by applying state estimates obtained from the H∞ filter, better estimates of the noise mean and covariance can be achieved, which can improve the performance of estimation. The proposed approach makes the best use of the system knowledge without a priori information with modest computation cost, which makes it possible to be applied online. Finally, numerical examples are presented to show the efficiency of this approach.

Keywords： Noise estimate     H∞     filter     Target tracking    

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Development of an analytical model to estimate the churning losses in high-speed axial piston pumps

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-021-0671-1

Abstract: design stage of high-speed axial piston pumps, but accurate analytical models are not available to estimate

Keywords： axial piston pump     rotating parts     high rotational speed     churning losses     drag torque    

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Fast scaling approach based on cavitation conditions to estimate the speed limitation for axial piston

Qun CHAO, Jianfeng TAO, Junbo LEI, Xiaoliang WEI, Chengliang LIU, Yuanhang WANG, Linghui MENG

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 1,   Pages 176-185 doi: 10.1007/s11465-020-0616-0

Abstract: This paper presents a scaling law derived from an analytical cavitation model to estimate the speed limitations

Keywords： axial piston pump     cavitation     speed limitation     scaling law    

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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Frontiers of Structural and Civil Engineering 2020, Volume 14, Issue 3,   Pages 609-622 doi: 10.1007/s11709-020-0623-6

Abstract: This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage

Keywords： Artificial Neural Networks     seismic vulnerability     masonry buildings     damage estimation     vulnerability curves    

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Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 4, doi: 10.1007/s11465-022-0703-5

Abstract: In fiber laser beam welding (LBW), the selection of optimal processing parameters is challenging and plays a key role in improving the bead geometry and welding quality. This study proposes a multi-objective optimization framework by combining an ensemble of metamodels (EMs) with the multi-objective artificial bee colony algorithm (MOABC) to identify the optimal welding parameters. An inverse proportional weighting method that considers the leave-one-out prediction error is presented to construct EM, which incorporates the competitive strengths of three metamodels. EM constructs the correlation between processing parameters (laser power, welding speed, and distance defocus) and bead geometries (bead width, depth of penetration, neck width, and neck depth) with average errors of 10.95%, 7.04%, 7.63%, and 8.62%, respectively. On the basis of EM, MOABC is employed to approximate the Pareto front, and verification experiments show that the relative errors are less than 14.67%. Furthermore, the main effect and the interaction effect of processing parameters on bead geometries are studied. Results demonstrate that the proposed EM-MOABC is effective in guiding actual fiber LBW applications.

Keywords： laser beam welding     parameter optimization     metamodel     multi-objective    

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A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

Frontiers of Structural and Civil Engineering 2022, Volume 16, Issue 8,   Pages 990-1002 doi: 10.1007/s11709-022-0864-7

Abstract: This study examined the feasibility of using the grey wolf optimizer (GWO) and artificial neural network (ANN) to predict the compressive strength (CS) of self-compacting concrete (SCC). The ANN-GWO model was created using 115 samples from different sources, taking into account nine key SCC factors. The validation of the proposed model was evaluated via six indices, including correlation coefficient (R), mean squared error, mean absolute error (MAE), IA, Slope, and mean absolute percentage error. In addition, the importance of the parameters affecting the CS of SCC was investigated utilizing partial dependence plots. The results proved that the proposed ANN-GWO algorithm is a reliable predictor for SCC’s CS. Following that, an examination of the parameters impacting the CS of SCC was provided.

Keywords： artificial neural network     grey wolf optimize algorithm     compressive strength     self-compacting concrete    

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Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Frontiers of Structural and Civil Engineering 2013, Volume 7, Issue 2,   Pages 133-136 doi: 10.1007/s11709-013-0202-1

Abstract: article examines the capability of Gaussian process regression (GPR) for prediction of effective stress parametervolumetric water content ( ), residual water content ( ), bubbling pressure ( ), suction ( ) and fitting parameterA sensitivity analysis has been done to determine the effect of each input parameter on .

Keywords： unsaturated soil     effective stress parameter     Gaussian process regression (GPR)     artificial neural network    

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1000 MW ultra-supercritical turbine steam parameter optimization

FENG Weizhong

Frontiers in Energy 2008, Volume 2, Issue 2,   Pages 187-193 doi: 10.1007/s11708-008-0030-5

Abstract: The 2 × 1000 MW ultra-supercritical steam turbine of Shanghai Waigaoqiao Phase III project, which uses grid frequency regulation and overload control through an overload valve, is manufactured by Shanghai Turbine Company using Siemens technology. Through optimization, the steam pressure is regarded as the criterion between constant pressure and sliding pressure operation. At high circulating water temperature, the turbine overload valve is kept closed when the unit load is lower than 1000 MW while at other circulating water temperatures the turbine can run in sliding pressure operation when the unit load is higher than 1000 MW and the pressure is lower than 27 MPa This increases the unit operation efficiency. The 3D bending technology in the critical piping helps to reduce the project investment and minimize the reheat system pressure drop which improves the unit operation efficiency and safety. By choosing lower circulating water design temperature and by setting the individual Boiler Feedwater Turbine condenser to reduce the exhaust steam flow and the heat load to the main condenser, the unit average back pressure and the terminal temperature difference are minimized. Therefore, the unit heat efficiency is increased.
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Suitability of common models to estimate hydrology and diffuse water pollution in North-eastern German

Muhammad WASEEM, Frauke KACHHOLZ, Jens TRÄNCKNER

Frontiers of Agricultural Science and Engineering 2018, Volume 5, Issue 4,   Pages 420-431 doi: 10.15302/J-FASE-2018243

Abstract:

Various process-based models are extensively being used to analyze and forecast catchment hydrology and water quality. However, it is always important to select the appropriate hydrological and water quality modeling tools to predict and analyze the watershed and also consider their strengths and weaknesses. Different factors such as data availability, hydrological, hydraulic, and water quality processes and their desired level of complexity are crucial for selecting a plausible modeling tool. This review is focused on suitable model selection with a focus on desired hydrological, hydraulic and water quality processes (nitrogen fate and transport in surface, subsurface and groundwater bodies) by keeping in view the typical lowland catchments with intensive agricultural land use, higher groundwater tables, and decreased retention times due to the provision of artificial drainage. In this study, four different physically based, partially and fully distributed integrated water modeling tools, SWAT (soil and water assessment tool), SWIM (soil and water integrated model), HSPF (hydrological simulation program– FORTRAN) and a combination of tools from DHI (MIKE SHE coupled with MIKE 11 and ECO Lab), have been reviewed particularly for the Tollense River catchment located in North-eastern Germany. DHI combined tools and SWAT were more suitable for simulating the desired hydrological processes, but in the case of river hydraulics and water quality, the DHI family of tools has an edge due to their integrated coupling between MIKE SHE, MIKE 11 and ECO Lab. In case of SWAT, it needs to be coupled with another tool to model the hydraulics in the Tollense River as SWAT does not include backwater effects and provision of control structures. However, both SWAT and DHI tools are more data demanding in comparison to SWIM and HSPF. For studying nitrogen fate and transport in unsaturated, saturated, and river zone, HSPF was a better model to simulate the desired nitrogen transformation and transport processes. However, for nitrogen dynamics and transformations in shallow streams, ECO Lab had an edge due its flexibility for inclusion of user-desired water quality parameters and processes. In the case of SWIM, most of the input data and governing equations are similar to SWAT but it does not include water bodies (ponds and lakes), wetlands and drainage systems. In this review, only the processes that were needed to simulate the Tollense River catchment were considered, however the resulted model selection criteria can be generalized to other lowland catchments in Australia, North-western Europe and North America with similar complexity.

Keywords： diffuse pollution     ECO Lab     HSPF     lowland catchment     MIKE 11     MIKE SHE     modeling tools     SWAT     SWIM     Tollense River     water quality    

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Structural parameter design method for a fast-steering mirror based on a closed-loop bandwidth

Guozhen CHEN, Pinkuan LIU, Han DING

Frontiers of Mechanical Engineering 2020, Volume 15, Issue 1,   Pages 55-65 doi: 10.1007/s11465-019-0545-y

Abstract: This study proposes a structural parameter design approach for an FSM system based on the quantitative

Keywords： fast-steering mirror     structural parameter     PI controller     bandwidth     notch filter    

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Energy efficient cutting parameter optimization

Xingzheng CHEN, Congbo LI, Ying TANG, Li LI, Hongcheng LI

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 2,   Pages 221-248 doi: 10.1007/s11465-020-0627-x

Abstract: Over the past 10 years, many researchers have been engaged in energy efficient cutting parameter optimizationThis paper conducts a comprehensive literature review of current studies on energy efficient cutting parameterCurrent studies on energy efficient cutting parameter optimization by using experimental design methodCombined with the current status, future research directions of energy efficient cutting parameter optimization

Keywords： energy efficiency     cutting parameter     optimization     machining process    

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Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator

Xueping PAN, Ping JU, Feng WU, Yuqing JIN

Frontiers of Mechanical Engineering 2017, Volume 12, Issue 3,   Pages 367-376 doi: 10.1007/s11465-017-0429-y

Abstract:

A new hierarchical parameter estimation method for doubly fed induction generator (DFIG) and drive

Keywords： wind turbine generator     DFIG     drive train system     hierarchical parameter estimation method     trajectory sensitivity    

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Variational mode decomposition based modal parameter identification in civil engineering

Mingjie ZHANG, Fuyou XU

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 5,   Pages 1082-1094 doi: 10.1007/s11709-019-0537-3

Abstract: An out-put only modal parameter identification method based on variational mode decomposition (VMD) isconducted to demonstrate the efficiency and highlight the superiority of the proposed method in modal parameterThe proposed method is proved to be efficient and accurate in modal parameter identification for bothlinear and nonlinear civil structures, including structures with closely spaced modes, sudden modal parameter

Keywords： modal parameter identification     variational mode decomposition     civil structure     nonlinear system     closely    

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Shallow foundation response variability due to soil and model parameter uncertainty

Prishati RAYCHOWDHURY,Sumit JINDAL

Frontiers of Structural and Civil Engineering 2014, Volume 8, Issue 3,   Pages 237-251 doi: 10.1007/s11709-014-0242-1

Abstract: The present work intends to investigate the effect of soil and model parameter uncertainty on the response

Keywords： shallow foun dation     sensitivity analysis     centrifuge data     first-order-second-moment (FOSM) method     parameter    

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Title Author Date Type Operation

An Estimate Method of Parametric in Reliability Engineering

Han Ming

Journal Article

Anovel approach of noise statistics estimate using H∞ filter in target tracking

Xie WANG,Mei-qin LIU,Zhen FAN,Sen-lin ZHANG

Journal Article

Development of an analytical model to estimate the churning losses in high-speed axial piston pumps

Journal Article

Fast scaling approach based on cavitation conditions to estimate the speed limitation for axial piston

Qun CHAO, Jianfeng TAO, Junbo LEI, Xiaoliang WEI, Chengliang LIU, Yuanhang WANG, Linghui MENG

Journal Article

The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for

Tiago Miguel FERREIRA, João ESTÊVÃO, Rui MAIO, Romeu VICENTE

Journal Article

Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC

Journal Article

A hybrid machine learning model to estimate self-compacting concrete compressive strength

Hai-Bang LY; Thuy-Anh NGUYEN; Binh Thai PHAM; May Huu NGUYEN

Journal Article

Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach

Pijush Samui, Jagan J

Journal Article

1000 MW ultra-supercritical turbine steam parameter optimization

FENG Weizhong

Journal Article

Suitability of common models to estimate hydrology and diffuse water pollution in North-eastern German

Muhammad WASEEM, Frauke KACHHOLZ, Jens TRÄNCKNER

Journal Article

Structural parameter design method for a fast-steering mirror based on a closed-loop bandwidth

Guozhen CHEN, Pinkuan LIU, Han DING

Journal Article

Energy efficient cutting parameter optimization

Xingzheng CHEN, Congbo LI, Ying TANG, Li LI, Hongcheng LI

Journal Article

Hierarchical parameter estimation of DFIG and drive train system in a wind turbine generator

Xueping PAN, Ping JU, Feng WU, Yuqing JIN

Journal Article

Variational mode decomposition based modal parameter identification in civil engineering

Mingjie ZHANG, Fuyou XU

Journal Article

Shallow foundation response variability due to soil and model parameter uncertainty

Prishati RAYCHOWDHURY,Sumit JINDAL

Journal Article