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作者： Amin Daghighi Cleveland State University

学位级别：硕士

The Great Lakes are most important freshwater bodies providing water resources and other various related businesses to the northeastern part of North America. However, harmful algal blooms (HABs) are more often and severe in those lakes than before and thus threatening lake environments and economies. Researchers have studied the factors influencing HABs characteristics using different scientific methods. In this study, all possible predictors and predictand variables were collected from various data source and then eight final predictors and one predictand were selected based on correlation between predictors and predictand variables. This study tests two machine learning techniques, Stepwise Multiple Regression (SMR) and Genetic Programming (GP), to forecast monthly HAB indicators in Western Lake Erie from July to October. SMR and GP models were created with selected input variables for two training periods, 2002 to 2011 and 2002 to 2014. A Spearman rank correlation coefficient was used to choose input variable sets for each HAB month considering 224 different combinations of lag time and average periods. The SMR models showed a correlation coefficient increase from 0.71 to 0.78 when extending the training period. The GP models followed a similar trend increasing the overall correlation coefficient from 0.82 to 0.96. Both models optimally selected monthly discharge and phosphorus mass from Maumee River Basin as significant predictor variables. A major drawback of both models was data-dependency as common in data-driven methods. GP was better to detect high nonlinear HAB mechanism than SMR due to its nature to use many mathematical functions while SMR only use the linear combination of variables. This study attested that both SMR and GP can be useful to simulate historical HAB event and predict future HAB severity. In future work, to avoid under- or over-prediction for unobserved HAB mechanism regarding short training period, it is suggested to develop an extrapola

关键词： Harmful Algal Blooms Genetic Programming Stepwise Multiple Regression data-driven methods

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Wetland Water-Level Prediction Using ANN in Conjunction with Base-Flow Recession Analysis

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JOURNAL OF HYDROLOGIC ENGINEERING 2017年第1期22卷

作者： Rezaeianzadeh, Mehdi Kalin, Latif Anderson, Christopher J. Auburn Univ Sch Forestry & Wildlife Sci 602 Duncan Dr Auburn AL 36849 USA Auburn Univ Sch Forestry & Wildlife Sci Forest Hydrol 602 Duncan Dr Auburn AL 36849 USA

This study introduces two artificial neural network (ANN)-based methodologies to predict hourly water levels (WLs) in wetlands characterized by water tables at or near the surface that respond rapidly to precipitation. The first method makes use of hourly precipitation data and WL data from nearby sites. The second method is a combination of ANN, recursive digital filter, and recession curve method and does not require any nearby site. The proposed methods were tested at two headwater wetlands in coastal Alabama. Site 17 had two nearby sites whose WLs were highly correlated with Site 17's. The root-mean-square error and Nash-Sutcliffe efficiencies were 2.9 cm and 0.98, respectively, when the first method was applied to Site 17. The second method was tested at Site 32. For this, the WL time series was separated into quick-and slow-response components. A combination of ANN and base-flow separation methods proved to be very efficient for WL prediction at this site, especially when the duration of quick-response components of individual events was less than 6 h. The proposed methodologies, therefore, proved useful in predicting WLs in wetlands dominated by both surface water and groundwater. (C) 2015 American Society of Civil Engineers.

关键词： Wetland Hydroperiod data-driven methods Continuous and event-based models Recursive digital filter Recession curve Bartlett's and t-tests

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An evaluation of data-driven identification strategies for complex nonlinear dynamic systems

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NONLINEAR DYNAMICS 2016年第2期85卷 1297-1318页

作者： Brewick, Patrick T. Masri, Sami F. Univ So Calif Viterbi Sch Engn Los Angeles CA 90089 USA

The development of suitable mathematical models on the basis of dynamic measurements from dispersed structural systems that may be undergoing significant nonlinear behavior is an important and very challenging problem in the field of Applied Mechanics that has drawn the attention of numerous investigators and motivated the development of many approaches for extracting reduced-order, reduced-complexity models from such systems. However, even though numerous nonlinear system identification techniques that are focused on the class of problems encountered in the structural dynamics field have been developed over the past decades, there are no systematic studies available that rigorously compare the performance and fidelity of such methods under similar operating conditions, and when encountering challenging nonlinear phenomena (such as hysteresis) that are present in physical systems, at different scales. This paper explores a variety of data-driven identification techniques for complex nonlinear systems and provides a much needed critical comparison of the accuracy and performance of each method. The Volterra/Wiener neural network (VWNN), a more recent development in nonlinear identification, is featured and compared against several existing methods, including polynomial-based nonlinear estimators and other artificial neural network systems. A representative three degree-of-freedom structure with nonlinear restoring force elements is used as the primary means of comparison for the different methods, and a variety of nonlinear models were investigated, including bilinear hysteresis, polynomial stiffness, and Bouc-Wen hysteresis. Performance comparisons were based on the ability to estimate the acceleration responses for both training and testing simulations. The results showed that, in general, the VWNN provided better accuracy in its estimates for each model. The VWNN also performed best when evaluated for scenarios in which numerical integration is required to find ve

关键词： Nonlinear identification data-driven methods Neural networks Bouc-Wen Hysteresis

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data-Based Nonlinear Identification and Constitutive Modeling of Hysteresis in NiTiNOL and Steel Strands

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JOURNAL OF ENGINEERING MECHANICS 2016年第12期142卷 1-17页

作者： Brewick, Patrick T. Masri, Sami F. Carboni, Biagio Lacarbonara, Walter Univ Southern Calif Sonny Astani Dept Civil & Environm Engn 3620 South Vermont Ave Los Angeles CA 90089 USA Sapienza Univ Rome Dept Struct & Geotech Engn I-00184 Rome Italy

Several different data-driven strategies for nonlinear identification are applied to experimental data exhibiting various types of hysteretic behavior. The experimental data contain displacement and restoring force information for several tests conducted using different configurations of a rheological testing device with various assemblies of nickel titanium-Naval Ordnance Laboratory (NiTiNOL) and steel wire strands. Among the different configurations, the response of the wire strands shows three distinct forms of nonlinear behavior: classical quasi-linear softening hysteresis;strongly pinched, hardening hysteresis;and slightly pinched, hardening hysteresis. The data-driven methods applied for nonlinear identification include polynomial basis functions and neural networks. The polynomial basis nonlinear identification methods are used for the construction and characterization of reduced-order models to gain insight into the physical modeling of the hysteretic phenomena. The neural network methods are found to be more useful for predictive purposes, demonstrating an ability to produce accurate results on both training and testing data.

关键词： Hysteresis Wire ropes and strands Nonlinear identification data-driven methods Generalized models

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Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges

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ANNUAL REVIEWS IN CONTROL 2016年 42卷 63-81页

作者： Tidriri, Khaoula Chatti, Nizar Verron, Sylvain Tiplica, Teodor LARIS Ave Notre Dame du Lac F-49000 Angers France

Fault Diagnosis and Health Monitoring (FD-HM) for modern control systems have been an active area of research over the last few years. Model-based FD-HM computational approaches have been extensively developed to detect and locate faults by considering logical or mathematical description of the monitored process. However, because of parametric, measurement and model uncertainties, applicable approaches that endeavor to locate faults with great accuracy are likely to give false alarms. Recently, many research works have been conducted in order to tackle this issue by making a tradeoff between accuracy and robustness during the fault detection phase. Due to the recent advances in sensor technology, computational capabilities and dedicated software/hardware interfaces, data-driven FD-HM approaches have demonstrated that highly accurate fault detection is possible when the system monitoring data for nominal and degraded conditions are available. Therefore, it seems that more than one approach is usually required for developing a complete robust fault detection and diagnosis tool. In this paper, the features of different model-based and data-driven approaches are investigated separately as well as the existing works that attempted to integrate both of them. In this latter context, there have been only few works published in the literature and hence reviewing and discussing them is strongly motivated by providing a good reference for those interested in developing hybrid approaches for FD-HM. (C) 2016 International Federation of Automatic Control. Published by Elsevier Ltd. All rights reserved.

关键词： Fault detection Fault diagnosis data-driven methods Model-based methods Hybrid methods

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A review on degradation modelling and its engineering applications

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International Journal of Performability Engineering 2017年第3期13卷 299-314页

作者： Shahraki, Ameneh Forouzandeh Yadav, Om Parkash Liao, Haitao Department of Industrial and Manufacturing Engineering North Dakota State University FargoND58108 United States Department of Industrial Engineering University of Arkansas FayettevilleAR72701 United States

Degradation modeling is an effective approach for reliability assessment, remaining useful life prediction, maintenance planning, and prognostics health management. Degradation models are usually developed based on degradation data and/or prior understandings of physics behind degradation processes of products or systems. Further, the effects of environmental or operational conditions on degradation processes and the knowledge about the dependency between degradation processes help improve the explanatory capabilities of degradation models. This paper presents a comprehensive review of existing degradation modeling approaches commonly used in engineering applications. To assist practitioners in understanding the concept of degradation modelling, the existing methods are classified into two broad categories: the data-driven approach and physics-based modelling approach. By systematically reviewing these approaches, we highlight their merits, useful applications and limitations. Finally, we provide a summary and indicate several future research challenges in this important area of reliability engineering. © 2017 Totem Publisher, Inc. All rights reserved.

关键词： data-driven methods Degradation modelling Multiple degradation processes Physics-based approach Random shock

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Leak localization in water distribution networks using a mixed model-based/data-driven approach

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CONTROL ENGINEERING PRACTICE 2016年第0期55卷 162-173页

作者： Soldevila, Adria Blesa, Joaquim Tornil-Sin, Sebastian Duviella, Eric Fernandez-Canti, Rosa M. Puig, Vicenc Res Ctr Supervis Safety & Automat Control CS2AC Rambla St Nebridi S-N Terrassa 08022 Spain CSIC UPC Inst Robot & Informat Ind Carrer Llorens Artigas 4-6 Barcelona 08028 Spain Mines DOUAI IA F-59508 Douai France Univ Lille F-59000 Lille France

This paper proposes a new method for leak localization in water distribution networks (WDNs). In a first stage, residuals are obtained by comparing pressure measurements with the estimations provided by a WDN model. In a second stage, a classifier is applied to the residuals with the aim of determining the leak location. The classifier is trained with data generated by simulation of the WDN under different leak scenarios and uncertainty conditions. The proposed method is tested both by using synthetic and experimental data with real WDNs of different sizes. The comparison with the current existing approaches shows a performance improvement. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Water distribution networks Leak localization Fault diagnosis Model-based methods data-driven methods Barcelona WDN

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data-driven robust receding horizon fault estimation

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AUTOMATICA 2016年 71卷 210-221页

作者： Wan, Yiming Keviczky, Tamas Verhaegen, Michel Gustafsson, Fredrik Delft Univ Technol Delft Ctr Syst & Control NL-2628 CD Delft Netherlands Linkoping Univ Dept Elect Engn SE-58183 Linkoping Sweden

This paper presents a data-driven receding horizon fault estimation method for additive actuator and sensor faults in unknown linear time-invariant systems, with enhanced robustness to stochastic identification errors. State-of-the-art methods construct fault estimators with identified state-space models or Markov parameters, without compensating for identification errors. Motivated by this limitation, we first propose a receding horizon fault estimator parameterized by predictor Markov parameters. This estimator provides (asymptotically) unbiased fault estimates as long as the subsystem from faults to outputs has no unstable transmission zeros. When the identified Markov parameters are used to construct the above fault estimator, stochastic identification errors appear as model uncertainty multiplied with unknown fault signals and online system inputs/outputs (I/O). Based on this fault estimation error analysis, we formulate a mixed-norm problem for the offline robust design that regards online I/O data as unknown. An alternative online mixed-norm problem is also proposed that can further reduce estimation errors at the cost of increased computational burden. Based on a geometrical interpretation of the two proposed mixed-norm problems, systematic methods to tune the user-defined parameters therein are given to achieve desired performance trade-offs. Simulation examples illustrate the benefits of our proposed methods compared to recent literature. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： data-driven methods Fault estimation Receding horizon estimation Parameter uncertainty

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Improvement in artificial neural network-based estimation of grid connected photovoltaic power output

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RENEWABLE ENERGY 2016年 97卷 838-848页

作者： Huang, Chao Bensoussan, Alain Edesess, Michael Tsui, Kwok L. City Univ Hong Kong Dept Syst Engn & Engn Management Kowloon Hong Kong Peoples R China Univ Texas Dallas Sch Management Richardson TX 75083 USA City Univ Hong Kong Ctr Syst Informat Engn Kowloon Hong Kong Peoples R China

This paper presents a method to improve the accuracy of artificial neural network (ANN)-based estimation of photovoltaic (PV) power output by introducing two more inputs, solar zenith angle and solar azimuth angle, in addition to the most widely used environmental information, plane-of-array irradiance and module temperature. Solar zenith angle and solar azimuth angle define the solar position in the sky;hence, the loss of modeling accuracy due to impacts of solar angle-of-incidence and solar spectrum is reduced or eliminated. The observed data from two sites where local climates are significantly different is used to train and test the proposed network. The good performance of the proposed network is verified by comparing with existing ANN model, algebraic model, and polynomial regression model which use environmental information only of plane-of-array irradiance and module temperature. Our results show that the proposed ANN model greatly improves the accuracy of estimation in the long term under various weather conditions. It is also demonstrated that the improvement in estimating outdoor PV power output by adding solar zenith angle and azimuth angle as inputs is useful for other data-driven methods like support vector machine regression and Gaussian process regression. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Photovoltaic Power output Artificial neural network Solar zenith angle Solar azimuth angle data-driven methods

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data-driven output-feedback fault-tolerant L₂ control of unknown dynamic systems

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ISA TRANSACTIONS 2016年第0期63卷 182-195页

作者： Wang, Jun-Sheng Yang, Guang-Hong Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Liaoning Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Liaoning Peoples R China

This paper studies the data-driven output-feedback fault-tolerant L-2-control problem for unknown dynamic systems. In a framework of active fault-tolerant control (FTC), three issues are addressed, including fault detection, controller reconfiguration for optimal guaranteed cost control, and tracking control. According to the data-driven form of observer-based residual generators, the system state is expressed in the form of the measured input-output data. On this basis, a model-free approach to L-2 control of unknown linear time-invariant (LTI) discrete-time plants is given. To achieve tracking control, a design method for a pre-filter is also presented. With the aid of the aforementioned results and the input-output data-based time-varying value function approximation structure, a data-driven FTC scheme ensuring L-2-gain properties is developed. To illustrate the effectiveness of the proposed methodology, two simulation examples are employed. (C) 2016 ISA. Published by Elsevier Ltd. All rights reserved.

关键词： data-driven methods Fault-tolerant control L-2-gain performance Output-feedback control

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