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ASME Turbomachinery Technical Conference and Exposition (Turbo Expo)

作者： Takiguchi, Yu Shiono, Satoshi Honda R&D Wako Saitama Japan

ISBN: (纸本)9780791884140

This paper presents a new method named Split Training Method (STM) for generating a data driven model of gas turbine engines to be used for anomaly detection and prognosis. The data driven model in this study simulates the relationship among engine parameters, such as sensor outputs and control inputs from pilots. More accurate trend monitoring of the relationship among engine parameters enables earlier anomaly detection with high accuracy. Machine learning techniques such as clustering, nonlinear regression, classification and optimization are used in STM. The input data for generating a simulation model is extracted from the training data by applying clustering algorithm to split into some clusters and outliers. This is why our method is named "Split" Training. Outliers are excluded from training data and each cluster of training data generates a simulation model with nonlinear regression. data points in the test data are classified to each cluster and used for evaluation of the corresponding model accuracy. In order to generate the simulation model with the highest accuracy, training data extraction is optimized by changing clustering shapes. The shapes of the optimized clusters are unique to individual engines and simulation target parameters. The novelty of STM is that the training data is selected in accordance with the characteristics of the data itself and not selected on the condition determined in advance. The result of comparison between STM and the conventional method shows significant accuracy enhancement.

关键词： data driven model Full flight data Anomaly detection Prognosis Machine learning Clustering Regression Classification Optimization DBSCAN Support vector regression

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A new data-driven model for post-transplant antibody dynamics in high risk kidney transplantation

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MATHEMATICAL BIOSCIENCES 2017年 284卷 3-11页

作者： Zhang, Yan Briggs, David Lowe, David Mitchell, Daniel Daga, Sunil Krishnan, Nithya Higgins, Robert Khovanova, Natasha Univ Warwick Sch Engn Warwick England NHS Blood & Transplant Birmingham W Midlands England Royal Liverpool Univ Hosp Dept Histocompatibil & Immunogenet Liverpool Merseyside England Univ Warwick Clin Sci Res Labs Coventry W Midlands England Univ Hosp Coventry & Warwickshire Renal Unit Coventry W Midlands England

The dynamics of donor specific human leukocyte antigen antibodies during early stage after kidney transplantation are of great clinical interest as these antibodies are considered to be associated with short and long term clinical outcomes. The limited number of antibody time series and their diverse patterns have made the task of modelling difficult. Focusing on one typical post-transplant dynamic pattern with rapid falls and stable settling levels, a novel data-driven model has been developed for the first time. A variational Bayesian inference method has been applied to select the best model and learn its parameters for 39 time series from two groups of graft recipients, i.e. patients with and without acute antibody-mediated rejection (AMR) episodes. Linear and nonlinear dynamic models of different order were attempted to fit the time series, and the third order linear model provided the best description of the common features in both groups. Both deterministic and stochastic parameters are found to be significantly different in the AMR and no-AMR groups showing that the time series in the AMR group have significantly higher frequency of oscillations and faster dissipation rates. This research may potentially lead to better understanding of the immunological mechanisms involved in kidney transplantation. (C) 2016 The Authors. Published by Elsevier Inc.

关键词： Kidney transplantation Antibody dynamics Ordinary differential equations data driven model Eigenvalue Variational Bayesian inference

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data-driven model Development Using Support Vector Machine for Railway Overhead Contact Wire Maintenance

Data-Driven Model Development Using Support Vector Machine f...

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IEEE International Conference on Industrial Engineering and Engineering Management IEEM

作者： Yuan, F. Q. Lu, J. M. Univ Tromso Dept Engn & Safety Tromso Norway

ISBN: (纸本)9781467380669

The fault on Overhead Contact Wire (OCW) causes traffic disruptions in railway transportation. The inspection and maintenance on OCW is inconvenient as it locates highly above the ground. Condition monitoring system has been installed to monitor the status of the OCW automatically. Huge amount of data have been collected by these systems. However, a general problem of these systems is the analysis on these data is weak. The condition monitoring system has been devalued due to this. This paper aims to develop data driven Support Vector Machine (SVM) model to extract useful information from the huge amount of data. This data driven model evaluate the OCW status without requiring understanding the physical mechanism of the fault. Maintenance points are suggested based on the output of the SVM model.

关键词： data driven model maintenance optimization Overhead Contact Wire (OCW) Support Vector Machine (SVM)

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A data driven epidemic model to analyse the lockdown effect and predict the course of COVID-19 progress in India

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CHAOS SOLITONS & FRACTALS 2020年 139卷 110034-110034页

作者： Sahoo, Bijay Kumar Sapra, Balvinder Kaur Bhabha Atom Res Ctr Radiol Phys & Advisory Div Mumbai 400094 Maharashtra India Homi Bhabha Natl Inst Dept Atom Energy Mumbai 400094 Maharashtra India

We propose a data driven epidemic model using the real data on the infection, recovery and death cases for the analysis of COVID-19 progression in India. The model assumes continuation of existing control measures such as lockdown and quarantines, the suspected and confirmed cases and does not consider the scenario of 2nd surge of the epidemic due to any reason. The model is arrived after least square fitting of epidemic behaviour model based on theoretical formulation to the real data of cumulative infection cases reported between 24 March 2020 and 30May 2020. The predictive capability of the model has been validated with real data of infection cases reported during June 1-10, 2020. A detailed analysis of model predictions in terms of future trend of COVID-19 progress individually in 18 states of India and India as a whole has been attempted. Infection rate in India, as a whole, is continuously decreasing with time and has reached 3 times lower than the initial infection rate after 6 weeks of lock down suggesting the effectiveness of the lockdown in containing the epidemic. Results suggest that India, as a whole, could see the peak and end of the epidemic in the month of July 2020 and March 2021 respectively as per the current trend in the data. Active infected cases in India may touch 2 lakhs or little above at the peak time and total infected cases may reach over 19 lakhs as per current trend. State-wise results have been discussed in the manuscript. However, the prediction may deviate particularly for longer dates, as assumptions of model cannot be met always in a real scenario. In view of this, a real time application (COV-IND Predictor) has been developed which automatically syncs the latest data from the national COVID19 dash board on daily basis and updates the model input parameters and predictions instantaneously. This real time application can be accessed from the link: https://***/spreadsheets/d/1fCwgnQ-dz4J0YWVDHUcbEW1423wOJjdEXm8TqJDWNAk/ed

关键词： COVID-19 data driven model Infected cases Cross correlation Time-lag analysis Least square fitting Mean recovery time Prediction Peak time End time Peak infected cases

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Novel data-driven stochastic model for antibody dynamics in kidney transplantation ∗

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IFAC-PapersOnLine 2015年第20期48卷 249-254页

作者： Yan Zhang David Lowe David Briggs Robert Higgins Natasha Khovanova School of Engineering University of Warwick UK NHS Blood and Transplant Birmingham University Hospitals Coventry and Warwickshire NHS Trust Coventry

Falls in the serum levels of donor specific HLA antibodies (DSA) after kidney transplantation are of great clinical interest, as they are associated with resolution of rejection and good long term outcomes in patients at high risk of graft loss. A data-driven model in the form of third order differential equation has been developed to describe the dynamics of the falls in DSA after renal transplantation. The model characterises the post transplant DSA behaviour for two groups of renal transplant recipients: those who experienced acute antibody mediated rejection (AMR) in the first days after operation and those who did not. A variational Bayesian inference method was employed to find the form of the model, infer the system parameters and extract the information of the recognisable patterns and the common features in DSA post transplant dynamics. Three models of different order have been investigated, and the third order linear model with four parameters outperformed the models of lower orders. The inferred deterministic parameters were found to be significantly different between the two groups of people with and without AMR. The eigenvalues for each DSA time series have been calculated and compared between the groups. A higher frequency of oscillation and a faster dissipation rate of antibodies have been found in the AMR group, which demonstrate a potential for intelligent laboratory interrogation of the underlying immunological mechanisms, which at present are entirely opaque.

关键词： Kidney transplantation Antibody dynamics Ordinary differential equations data driven model Eigenvalue Variational Bayesian inference

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data driven modelling approach for design assessment of spacecraft equipment

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APPLIED MATHEMATICAL modelLING 2025年 140卷

作者： Yu, Changshuai Zhu, Yun-Peng Luo, Haitao Luo, Zhong Chinese Acad Sci Shenyang Inst Automat Key Lab Robot Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110169 Peoples R China Queen Mary Univ London Sch Engn & Mat Sci London England Northeastern Univ Sch Mech Engn & Automat Shenyang 110819 Liaoning Peoples R China

Ground vibration tests are conducted on spacecraft equipment prior to launch to simulate the demanding vibration loads, characterized by high amplitudes and energies, that occur during rocket launching. These vibration loads may be destructive to spacecraft equipment. Therefore, spacecraft equipment must be assessed under these demanding loads before undergoing destructive ground vibration tests. Existing assessment methods, whether based on finite element analysis or other physical techniques, struggle to quantitatively analyze the nonlinear dynamic behavior of spacecraft equipment, such as the frequency-drift phenomenon, due to their complex structures. The objective of this study was to develop a data driven model to implement the design assessment of spacecraft equipment based on training data obtained from non-destructive tests. However, an identified data driven model is prone to overfitting due to noise effects in system identification, which may destabilize the model when subjected to demanding loads with larger amplitudes. To address this issue, a novel regularization approach based data driven modelling approach for the design assessment of spacecraft equipment was proposed. In this approach, the system identification algorithm is regularized by model predicted output errors of the training inputs and the demanding loads for design assessment. Then, an iterative computational approach was used to automatically discard unstable data driven models, following which the efficiency of the proposed data driven modelling approach was verified by comparison with other traditional methods for an engineering case of large spacecraft equipment. The results demonstrated that the proposed method could address the instability problem in extrapolation predictions and demonstrated superiority over traditional methods.

关键词： Spacecraft equipment Ground vibration tests Design assessment Frequency drift phenomenon data driven model

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Which one is more important in daily runoff forecasting using data driven models: Input data, model type, preprocessing or data length?

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JOURNAL OF HYDROLOGY 2022年 606卷 127429-127429页

作者： Moosavi, Vahid Fard, Zeinab Gheisoori Vafakhah, Mehdi Tarbiat Modares Univ Fac Nat Resources & Marine Sci Dept Watershed Management Engn Tehran Iran

Rainfall-runoff modeling is of great importance in hydrological sciences. Several different models have been developed for runoff modeling in three main categories i.e. physically-based, conceptual and empirical models. data driven models are of the most widely used models in runoff modeling besides process based models. Different studies have been done to assess the performance of various models and the effect of input datasets, data length and disparate signal processing methods on the modeling performance. However, each of these studies has examined one of these factors separately and didn't assess the effect of these factors on the accuracy of runoff forecasting. Therefore, assessing the importance of each of the mentioned factors as well as determining the optimum structure that produces the best accuracy is still challenging. The main aim of this study was to determine the importance and the optimal combination of these factors in daily runoff modeling. In order to achieve this goal, Taguchi method was used. First, five levels were defined for each of the abovementioned factors. Five different input data combinations, five data driven models i.e. Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Regression (SVR), Group Method of data Handling (GMDH), Random Forest (RF) and Partial Least Square Regression (PLS), four different signal processing methods i.e. normalization, wavelet, ensemble empirical mode decomposition (EEMD) and singular spectrum analysis (SSA) as well as no pre-processing condition, and five data lengths i.e. 2, 5, 10, 15 and 20 years were considered. The L-25 Taguchi orthogonal array was selected accordingly. The required 25 tests were implemented according to the L-25 Taguchi orthogonal array in three different basins to achieve more generalizable results. The results were then used in Taguchi analysis in order to attain the optimal combination of the levels of the mentioned factors and the importance of these factors in accurate

关键词： Artificial intelligence data driven model Optimization Signal processing Taguchi method

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data and model driven Task Offloading Strategy in the Dynamic Mobile Edge Computing System

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Journal of Systems Science & Complexity 2024年第1期37卷 351-368页

作者： DONG Hairong WU Wei SONG Haifeng LIU Zhen ZHANG Zixuan School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering BeiHang UniversityBeijing 100191China CRSC Research&Design Institute Group Co. Ltd.Beijing 100070China

Mobile Edge Computing(MEC)provides communication and computational capabilities for the industrial Internet,meeting the demands of latency-sensitive ***,traditional model-driven task offloading strategies face challenges in adapting to situations with unknown network communication status and computational *** limitation becomes notably significant in complex industrial networks of high-speed *** by these considerations,a data and model-driven task offloading problem is proposed in this paper.A redundant communication network is designed to adapt to anomalous channel states when tasks are offloaded to edge *** link switching mechanism is executed by the train according to the attributes of the completed *** task offloading optimization problem is formulated by introducing data-driven prediction of communication states into the traditional ***,the optimal strategy is achieved by employing the informer-based prediction algorithm and the quantum particle swarm optimization method,which effectively tackle real-time optimization problems due to their low time *** simulations illustrate that the data and model-driven task offloading strategy can predict the communication state in advance,thus reducing the cost of the system and improving its robustness.

关键词： data driven model informer mobile edge computing quantum particle swarm optimization task offloading.

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A New data-driven Roll Force and Roll Torque model Based on FEM and Hybrid PSO-ELM for Hot Strip Rolling

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ISIJ INTERNATIONAL 2019年第9期59卷 1604-1613页

作者： Wang, Zhenhua Zhang, Dianhua Gong, Dianyao Peng, Wen Northeastern Univ State Key Lab Rolling & Automat Shenyang 110819 Liaoning Peoples R China

In this paper, a new Extreme Learning Machine (ELM) regression model of roll force and roll torque based on data-driven is proposed. The three-dimensional elastic-plastic finite element model (FEM) is established to solve the roll force and roll torque under different parameters (including rolling reduction rate, roll radius, rolling speed, average width of strip, entry temperature of strip). The regression model of ELM optimized by Particle Swarm Optimization (PSO) is established through using the datasets obtained by FEM. The PSO-ELM model prediction values of roll force and roll torque are compared with the single ELM and PSO-SVM model, and the error results of the prediction values are analyzed. The error results fully verify the feasibility and accuracy of the PSO-ELM model proposed. It is found that the new data-drive model of roll force and roll torque is simple in structure and it can make up for the deficiency of traditional mathematical mechanism model in dealing with nonlinear problems. The research result reveals that PSO-ELM method is suitable for parameters prediction and model optimization in strip rolling process.

关键词： data driven model finite element method extreme learning machine rolling force rolling torque

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A multi-level data-driven Bayesian approach for stochastic model updating of complex aeroelastic systems

A multi-level data-driven Bayesian approach for stochastic m...

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AIAA SciTech Forum

作者： McGurk, M. Yuan, J. Univ Strathclyde Aerosp Ctr Excellence Glasgow G1 1XJ Lanark Scotland Univ Southampton Computat Engn Design Grp Southampton Hants England

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

The objective of this work is to propose a multilevel data-driven Bayesian framework to minimise the sampling requirement for stochastic model identification of general nonlinear aeroelastic systems. Adaptive Kriging based surrogate models are developed through multi-level Bayesian modeling updating to represent limit cycle oscillation (LCO) response of the nonlinear aeroelastic model. The proposed methodology is demonstrated on an aerofoil wing flutter model together with LCO experimental testing data. The results is benchmarked by the counterpart from a classical single-level approach. It is observed that the proposed approach offered a 74% reduction in training data requirement/run time and 3% accuracy improvement for the surrogate model.

关键词： Nonlinear Aeroelastic Systems Bayesian Framework Uncertainty Quantification NACA 0015 data driven model Mathematical models Kriging model Harmonic Balance Maximum Likelihood Estimation Monte Carlo Simulation

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