Nancy Lybeck - Academia.edu (original) (raw)
Papers by Nancy Lybeck
Annual Conference of the PHM Society
Nuclear plant sites collect and store large volumes of data gathered from various equipment and s... more Nuclear plant sites collect and store large volumes of data gathered from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning (ML) and artificial intelligence (AI) technologies to provide diagnostic and prognostic capabilities within the nuclear power industry, thus reducing operations and maintenance (O&M) costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature plant closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging ML and AI technologies to develop data-driven algorithms that better diagnose and predict potential faults within the system. Improved model accuracy can help reduce unnecessary maintenance and foster more efficient planning of future maintenance, ther...
The research and development reported here is part of the Technology Enabled Risk-Informed Mainte... more The research and development reported here is part of the Technology Enabled Risk-Informed Maintenance Strategy project sponsored by the U.S. Department of Energy's Light Water Reactor Sustainability program. The primary objective of the research presented in this report is to produce a technical basis for developing explainable and trustable artificial intelligence (AI) and machine learning (ML) technologies. The technical basis will lay the foundation for addressing the technical and regulatory adoption challenges of AI/ML technologies across plant assets and the nuclear industry at scale and to achieve seamless cost-effective automation without compromising plant safety and reliability. The technical basis ensuring wider adoption of AI/ML technologies presented in this report was developed by Idaho National Laboratory (INL), in collaboration with Public Service Enterprise Group (PSEG) Nuclear, LLC. To develop the initial technical basis, the circulating water system (CWS) at the PSEG-owned plant sites was selected as the identified plant asset. Specifically, the issue of waterbox fouling diagnosis in the CWS using different types of CWS data is presented to address the said challenge. The approach presented in this report is based on the closed-loop forward-backward process that tries to capture the advancements in data science addressing the explainability of AI/ML outcomes, user-centric interpretability of those outcomes, and how user interpretation can be used as feedback to further simplify the process. A prototype interface is developed to present a focused component-level display of the ML model outputs in a usable and digestible form.
International Journal of Prognostics and Health Management
Nuclear power plants collect and store large volumes of heterogeneous data from various component... more Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory ...
Nuclear power plants (NPPs) collect and store large volumes of heterogeneous data from various co... more Nuclear power plants (NPPs) collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict the future condition of equipment. Maintenance operations can then be planned in advance whenever degraded performance is predicted, resulting in fewer unplanned outages and the optimization of maintenance activities. This helps reduce maintenance costs and improve the overall economics of nuclear power. This report focuses on Fiscal Year 2021 research progress and the development of a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances (in terms of RMSE) of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters at both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models, given new data from different systems.
The U.S. Department of Energy Very High Temperature Reactor Program is acquiring data in preparat... more The U.S. Department of Energy Very High Temperature Reactor Program is acquiring data in preparation for developing an Alloy 617 Code Case for inclusion in the nuclear section of the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel (B&PV) Code. A draft code case was previously developed, but effort was suspended before acceptance by ASME. As part of the draft code case effort, a database was compiled of yield and tensile strength data from tests performed in air. Yield strength and tensile strength at temperature are used to set time independent allowable stress for construction materials in B&PV Code, Section III, Subsection NH. The yield and tensile strength data used for the draft code case has been augmented with additional data generated by Idaho National Laboratory and Oak Ridge National Laboratory in the U.S. and CEA in France. The standard ASME Section II procedure for generating yield and tensile strength at temperature is presented, along with alt...
Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provid... more Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.
Operation and Maintenance (O&M) costs for nuclear power plants (NPPs) are very large. The nuclear... more Operation and Maintenance (O&M) costs for nuclear power plants (NPPs) are very large. The nuclear industry is beginning to see reactors shut down-even after their operating licenses have been extended-because they are not economically competitive with other energy sources. These early closures happen due to economic reasons, despite excellent safety records. Therefore, it is imperative to reduce costs in order to prevent these early closures. This paper showcases recent research on advanced fault diagnostics techniques and preventative maintenance optimization (PMO) for reducing NPP maintenance costs. This report focuses on the feedwater and condensate system (FWCS) for both pressurized-and boiling-water reactor (BWR) systems. The computerized maintenance management system (CMMS), which contains the plant's digital record of all corrective maintenance (CM) and preventative maintenance (PM) work orders, provided the ground truth for locating potential faults and labeling the process data as either healthy or faulted. Various feature extraction techniques were utilized to further differentiate the faulted data from the healthy data. Through a cross-validation procedure, support vectors machines (SVMs) were used to label other test sets of process data as either healthy or faulted. Similar faults were not found within the BWR system, thus opening up the potential for PMO, since an unnecessary amount of PM leads to inflated maintenance costs. This paper summarizes the steps for PMO, from component health determinations to recommendations for action. An example of PMO assessment is presented for condensate pumps (CPs), condensate booster pumps (CBPs), and the respective motors that drive them.
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Typescript. Thesis (Ph. D.)--Montana State University, 1994. Includes bibliographical references ... more Typescript. Thesis (Ph. D.)--Montana State University, 1994. Includes bibliographical references (leaves 99-101).
Kybernetika -Praha-
We report on our efforts to model nonlinear dynamics in elastomers. Our efforts include the devel... more We report on our efforts to model nonlinear dynamics in elastomers. Our efforts include the development of computational techniques for simulation studies and for use in inverse or system identification problems. As a first step towards the full dynamic case, we present the static inverse problem, with experimental results. We also present results from the simulation of dynamic experiments. 1 Introduction A problem of fundamental interest and great importance in modern material sciences is the development of new materials for use as both passive and active vibration suppression devices. Materials such as elastomers, rubber-like composites typically filled with inactive particles such as carbon black and silica, are frequently used in parts such as engine mounts and springs. One could imagine using active fillers, such as piezoelectric, magnetic, or conductive particles, to create a "smart" material which could be used as an active vibration suppression device. In the quest...
We report on our efforts to model nonlinear dynamics in elastomers. Our efforts include the devel... more We report on our efforts to model nonlinear dynamics in elastomers. Our efforts include the development of computational techniques for simulation studies and for use in inverse or system identification problems. 1 Introduction A problem of fundamental interest and great importance in modern material sciences is the development of both passive and active ("smart") vibration devices constructed from polymer (long molecular chains of covalently bonded atoms often having cross-linking chains) composites such as elastomers filled with carbon black and/or silica or with active elements (i.e., piezoelectric, magnetic or conductive particles). These rubber based products (even without active elements) involve very complex viscoelastic materials that are not at all like metals (where large deformations lead to permanent material changes) and do not satisfy the usual, well-developed linear theory of (infinitesimal) elasticity for deformable bodies. They typically exhibit mechanical...
Accurate modeling of the dynamic mechanical behavior of elastomers presents many challenges, incl... more Accurate modeling of the dynamic mechanical behavior of elastomers presents many challenges, including the nonlinear relationship between stress and strain, the loss of kinetic energy (damping), and the loss of potential energy (hysteresis). Currently available software packages for studying the stress-strain laws in rubber-like materials assume a form of the strain energy function (SEF), such as a cubic Mooney-Rivlin form or an Ogden form. While these methods can produce good results, they are only applicable to static behavior, and they ignore hysteresis and damping. We present a dynamic partial differential equation (PDE) formulation, with a Kelvin-Voigt damping term, as an alternative approach to the SEF formulation. Constitutive laws are estimated using data from simple extension experiments, leading to static results which compare favorably with results achieved by estimating a cubic Mooney-Rivlin SEF, and dynamic results which offer new insight. A neo-Hookean model for genera...
Contemporary Mathematics, 1994
ABSTRACT The solution of elliptic problems using domain decomposition techniques has been of grea... more ABSTRACT The solution of elliptic problems using domain decomposition techniques has been of great interest in recent years. Sinc basis functions form a desirable basis to use in approaching domain decomposition for elliptic problems because they are especially well-suited for problems with boundary singularities, and both the Sinc-Galerkin and sinc-collocation methods converge exponentially. This paper deals with overlapping and patching domain decomposition used in conjunction with the Sinc-Galerkin method for both the two-point boundary value problem and Poisson’s equation on a rectangle.
2007 IEEE Aerospace Conference, 2007
ABSTRACT The ultimate goal of prognostics is to accurately predict remaining useful life (RUL) ba... more ABSTRACT The ultimate goal of prognostics is to accurately predict remaining useful life (RUL) based on sensor data, system usage, and prior knowledge of fault-to-failure progression rates (i.e. a model). One of the key components necessary for developing a prognostic algorithm is a diagnostic severity metric. This paper presents an evaluation of a number of standard vibration-based diagnostic metrics, utilizing a large set of experimental fault-to-failure progression data for bearings. These experiments included over 40 complete bearing failure progressions with 10 to 30 ground truth data points per bearing. Additional data supporting the potential of using oil debris monitoring in conjunction with vibration monitoring is also presented. Once a prognostic algorithm has been developed, the next critical step is to validate how well the algorithm performs. Conceptually, this seems like a simple task. However, there are many criteria to be considered, including convergence rate, accuracy, and stability of the RUL prediction. The paper includes an evaluation of prognostic algorithms based on vibration-based diagnostics that feed into a model-based prediction of future spall propagation and thus remaining life. Methods for objectively measuring the quality of the predictions are proposed. The results presented herein help demonstrate the capabilities and limitations of predictive prognostics at the current state-of-the-art.
Systems and Control in the Twenty-First Century, 1997
There are an extensive body of knowledge and some commercial products available for calculating p... more There are an extensive body of knowledge and some commercial products available for calculating prognostics, remaining useful life, and damage index parameters. The application of these technologies within the nuclear power community is still in its infancy. Online monitoring and condition-based maintenance is seeing increasing acceptance and deployment, and these activities provide the technological bases for expanding to add predictive/prognostics capabilities. In looking to deploy prognostics there are three key aspects of systems that are presented and discussed: component/system/structure selection, prognostic algorithms, and prognostics architectures. Criteria are presented for component selection: feasibility, failure probability, consequences of failure, and benefits of the prognostics and health management (PHM) system. The basis and methods commonly used for prognostics algorithms are reviewed and summarized. Criteria for evaluating PHM architectures are presented: open, modular architecture; platform independence; graphical user interface for system development and/or results viewing; web-enabled tools; scalability; and standards compatibility. Thirteen software products were identified and discussed in the context of being potentially useful for deployment in a PHM program applied to systems in a nuclear power plant (NPP). These products were evaluated by using information available from company websites, product brochures, fact sheets, scholarly publications, and direct communication with vendors. The thirteen products were classified into four groups of software: research tools, PHM system development tools, deployable architectures, and peripheral tools. Eight software tools fell into the deployable architectures category. Of those eight, only two employ all six modules of a full PHM system. Five systems did not offer prognostic estimates, and one system employed the full health monitoring suite but lacked operations and maintenance support. Each product is briefly described in Appendix A, "Assessment Criteria." Selection of the most appropriate software package for a particular application will depend on the chosen component, system, or structure. Ongoing research will determine the most appropriate choices for a successful demonstration of PHM systems in aging NPPs.
Computation and Control IV, 1995
... 254 N. LYBECK AND K. BOWERS Proof of this theorem is given in [3]. Concatenating each side of... more ... 254 N. LYBECK AND K. BOWERS Proof of this theorem is given in [3]. Concatenating each side of the equation in step 3 yields The equation in step 5 ... Let the sinc error, assuming the last iterate is u2A/, be defined by < max l,(. r, y) es \\ ES\\= max< max| u (*, y)- where for p= 1, 2 ...
2006 IEEE Aerospace Conference
A fundamental problem in the development and validation of PHM technologies is the general shorta... more A fundamental problem in the development and validation of PHM technologies is the general shortage of realistic fault signature data. While healthy signatures can be obtained from operational systems, faults are relatively rare and difficult to observe. The PHM community must rely primarily on bench level seeded fault test data collected under a limited set of conditions. To augment physical data, a modeling and simulation toolset is being developed for the vibration signatures of faulted components in propulsion subsystems such as gearboxes. The toolset includes sophisticated dynamic models of vibration forcing for common rotating components such as bearings and gears based on detailed analysis of their physical interactions, including the effects of faults such as bearing spalling or gear tooth cracks. The response of the overall system, and thus the vibration signal seen at a particular sensor location can then be predicted using either FEA or a transfer function analysis of actual hardware. The purpose of the toolset is to leverage seeded fault test data (for example to study operating conditions or fault types that were not tested), improve fault diagnosability through optimal sensor placement, and enhance development, testing, and validation of diagnostic systems. Several examples are presented comparing simulated vibration signals to actual test data. 1,2 TABLE OF CONTENTS 1. INTRODUCTION .
Annual Conference of the PHM Society
Nuclear plant sites collect and store large volumes of data gathered from various equipment and s... more Nuclear plant sites collect and store large volumes of data gathered from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning (ML) and artificial intelligence (AI) technologies to provide diagnostic and prognostic capabilities within the nuclear power industry, thus reducing operations and maintenance (O&M) costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature plant closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging ML and AI technologies to develop data-driven algorithms that better diagnose and predict potential faults within the system. Improved model accuracy can help reduce unnecessary maintenance and foster more efficient planning of future maintenance, ther...
The research and development reported here is part of the Technology Enabled Risk-Informed Mainte... more The research and development reported here is part of the Technology Enabled Risk-Informed Maintenance Strategy project sponsored by the U.S. Department of Energy's Light Water Reactor Sustainability program. The primary objective of the research presented in this report is to produce a technical basis for developing explainable and trustable artificial intelligence (AI) and machine learning (ML) technologies. The technical basis will lay the foundation for addressing the technical and regulatory adoption challenges of AI/ML technologies across plant assets and the nuclear industry at scale and to achieve seamless cost-effective automation without compromising plant safety and reliability. The technical basis ensuring wider adoption of AI/ML technologies presented in this report was developed by Idaho National Laboratory (INL), in collaboration with Public Service Enterprise Group (PSEG) Nuclear, LLC. To develop the initial technical basis, the circulating water system (CWS) at the PSEG-owned plant sites was selected as the identified plant asset. Specifically, the issue of waterbox fouling diagnosis in the CWS using different types of CWS data is presented to address the said challenge. The approach presented in this report is based on the closed-loop forward-backward process that tries to capture the advancements in data science addressing the explainability of AI/ML outcomes, user-centric interpretability of those outcomes, and how user interpretation can be used as feedback to further simplify the process. A prototype interface is developed to present a focused component-level display of the ML model outputs in a usable and digestible form.
International Journal of Prognostics and Health Management
Nuclear power plants collect and store large volumes of heterogeneous data from various component... more Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This paper focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory ...
Nuclear power plants (NPPs) collect and store large volumes of heterogeneous data from various co... more Nuclear power plants (NPPs) collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict the future condition of equipment. Maintenance operations can then be planned in advance whenever degraded performance is predicted, resulting in fewer unplanned outages and the optimization of maintenance activities. This helps reduce maintenance costs and improve the overall economics of nuclear power. This report focuses on Fiscal Year 2021 research progress and the development of a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances (in terms of RMSE) of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters at both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models, given new data from different systems.
The U.S. Department of Energy Very High Temperature Reactor Program is acquiring data in preparat... more The U.S. Department of Energy Very High Temperature Reactor Program is acquiring data in preparation for developing an Alloy 617 Code Case for inclusion in the nuclear section of the American Society of Mechanical Engineers (ASME) Boiler and Pressure Vessel (B&PV) Code. A draft code case was previously developed, but effort was suspended before acceptance by ASME. As part of the draft code case effort, a database was compiled of yield and tensile strength data from tests performed in air. Yield strength and tensile strength at temperature are used to set time independent allowable stress for construction materials in B&PV Code, Section III, Subsection NH. The yield and tensile strength data used for the draft code case has been augmented with additional data generated by Idaho National Laboratory and Oak Ridge National Laboratory in the U.S. and CEA in France. The standard ASME Section II procedure for generating yield and tensile strength at temperature is presented, along with alt...
Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provid... more Recent advancements in machine learning (ML) and artificial intelligence (AI) technologies provide an opportunity for leveraging data-driven algorithms to predict future nuclear power plant (NPP) operating conditions by using recorded plant process data. Successfully implementing these models can lead to cost-reducing, conditioned-based predictive maintenance through optimized maintenance schedules and a reduction of unnecessary maintenance activities. This report discusses the verification and validation of short-term forecasting processes (i.e., data cleaning, feature selection, model optimization, and forecasting) developed in previous reports. The verification and validation (V&V) process demonstrates the expected precision and accuracy when the ML model encounters new datasets from different systems. Shapley additive explanations were used as the primary means of feature selection across these different data set. Individual models were trained for each data set, then validated through a cross-validation procedure. In this report, two different ML models were tasked to predict variables from three different plant process data sets with varying prediction horizons. The results indicate that support vector regression (SVR) outperformed long short-term memory (LSTM) neural networks in regard to each data set and each prediction horizon in this study, but further tuning and optimization could improve long short-term memory results. However, each forecasting model showed reduced performance as the prediction horizon was extended from 1 hour to 1 day ahead. Research is ongoing to evaluate the optimal input variable space, which is based on a given set of process parameters, to further improve forecasting accuracy.
Operation and Maintenance (O&M) costs for nuclear power plants (NPPs) are very large. The nuclear... more Operation and Maintenance (O&M) costs for nuclear power plants (NPPs) are very large. The nuclear industry is beginning to see reactors shut down-even after their operating licenses have been extended-because they are not economically competitive with other energy sources. These early closures happen due to economic reasons, despite excellent safety records. Therefore, it is imperative to reduce costs in order to prevent these early closures. This paper showcases recent research on advanced fault diagnostics techniques and preventative maintenance optimization (PMO) for reducing NPP maintenance costs. This report focuses on the feedwater and condensate system (FWCS) for both pressurized-and boiling-water reactor (BWR) systems. The computerized maintenance management system (CMMS), which contains the plant's digital record of all corrective maintenance (CM) and preventative maintenance (PM) work orders, provided the ground truth for locating potential faults and labeling the process data as either healthy or faulted. Various feature extraction techniques were utilized to further differentiate the faulted data from the healthy data. Through a cross-validation procedure, support vectors machines (SVMs) were used to label other test sets of process data as either healthy or faulted. Similar faults were not found within the BWR system, thus opening up the potential for PMO, since an unnecessary amount of PM leads to inflated maintenance costs. This paper summarizes the steps for PMO, from component health determinations to recommendations for action. An example of PMO assessment is presented for condensate pumps (CPs), condensate booster pumps (CBPs), and the respective motors that drive them.
Typescript. Thesis (Ph. D.)--Montana State University, 1994. Includes bibliographical references ... more Typescript. Thesis (Ph. D.)--Montana State University, 1994. Includes bibliographical references (leaves 99-101).
Kybernetika -Praha-
We report on our efforts to model nonlinear dynamics in elastomers. Our efforts include the devel... more We report on our efforts to model nonlinear dynamics in elastomers. Our efforts include the development of computational techniques for simulation studies and for use in inverse or system identification problems. As a first step towards the full dynamic case, we present the static inverse problem, with experimental results. We also present results from the simulation of dynamic experiments. 1 Introduction A problem of fundamental interest and great importance in modern material sciences is the development of new materials for use as both passive and active vibration suppression devices. Materials such as elastomers, rubber-like composites typically filled with inactive particles such as carbon black and silica, are frequently used in parts such as engine mounts and springs. One could imagine using active fillers, such as piezoelectric, magnetic, or conductive particles, to create a "smart" material which could be used as an active vibration suppression device. In the quest...
We report on our efforts to model nonlinear dynamics in elastomers. Our efforts include the devel... more We report on our efforts to model nonlinear dynamics in elastomers. Our efforts include the development of computational techniques for simulation studies and for use in inverse or system identification problems. 1 Introduction A problem of fundamental interest and great importance in modern material sciences is the development of both passive and active ("smart") vibration devices constructed from polymer (long molecular chains of covalently bonded atoms often having cross-linking chains) composites such as elastomers filled with carbon black and/or silica or with active elements (i.e., piezoelectric, magnetic or conductive particles). These rubber based products (even without active elements) involve very complex viscoelastic materials that are not at all like metals (where large deformations lead to permanent material changes) and do not satisfy the usual, well-developed linear theory of (infinitesimal) elasticity for deformable bodies. They typically exhibit mechanical...
Accurate modeling of the dynamic mechanical behavior of elastomers presents many challenges, incl... more Accurate modeling of the dynamic mechanical behavior of elastomers presents many challenges, including the nonlinear relationship between stress and strain, the loss of kinetic energy (damping), and the loss of potential energy (hysteresis). Currently available software packages for studying the stress-strain laws in rubber-like materials assume a form of the strain energy function (SEF), such as a cubic Mooney-Rivlin form or an Ogden form. While these methods can produce good results, they are only applicable to static behavior, and they ignore hysteresis and damping. We present a dynamic partial differential equation (PDE) formulation, with a Kelvin-Voigt damping term, as an alternative approach to the SEF formulation. Constitutive laws are estimated using data from simple extension experiments, leading to static results which compare favorably with results achieved by estimating a cubic Mooney-Rivlin SEF, and dynamic results which offer new insight. A neo-Hookean model for genera...
Contemporary Mathematics, 1994
ABSTRACT The solution of elliptic problems using domain decomposition techniques has been of grea... more ABSTRACT The solution of elliptic problems using domain decomposition techniques has been of great interest in recent years. Sinc basis functions form a desirable basis to use in approaching domain decomposition for elliptic problems because they are especially well-suited for problems with boundary singularities, and both the Sinc-Galerkin and sinc-collocation methods converge exponentially. This paper deals with overlapping and patching domain decomposition used in conjunction with the Sinc-Galerkin method for both the two-point boundary value problem and Poisson’s equation on a rectangle.
2007 IEEE Aerospace Conference, 2007
ABSTRACT The ultimate goal of prognostics is to accurately predict remaining useful life (RUL) ba... more ABSTRACT The ultimate goal of prognostics is to accurately predict remaining useful life (RUL) based on sensor data, system usage, and prior knowledge of fault-to-failure progression rates (i.e. a model). One of the key components necessary for developing a prognostic algorithm is a diagnostic severity metric. This paper presents an evaluation of a number of standard vibration-based diagnostic metrics, utilizing a large set of experimental fault-to-failure progression data for bearings. These experiments included over 40 complete bearing failure progressions with 10 to 30 ground truth data points per bearing. Additional data supporting the potential of using oil debris monitoring in conjunction with vibration monitoring is also presented. Once a prognostic algorithm has been developed, the next critical step is to validate how well the algorithm performs. Conceptually, this seems like a simple task. However, there are many criteria to be considered, including convergence rate, accuracy, and stability of the RUL prediction. The paper includes an evaluation of prognostic algorithms based on vibration-based diagnostics that feed into a model-based prediction of future spall propagation and thus remaining life. Methods for objectively measuring the quality of the predictions are proposed. The results presented herein help demonstrate the capabilities and limitations of predictive prognostics at the current state-of-the-art.
Systems and Control in the Twenty-First Century, 1997
There are an extensive body of knowledge and some commercial products available for calculating p... more There are an extensive body of knowledge and some commercial products available for calculating prognostics, remaining useful life, and damage index parameters. The application of these technologies within the nuclear power community is still in its infancy. Online monitoring and condition-based maintenance is seeing increasing acceptance and deployment, and these activities provide the technological bases for expanding to add predictive/prognostics capabilities. In looking to deploy prognostics there are three key aspects of systems that are presented and discussed: component/system/structure selection, prognostic algorithms, and prognostics architectures. Criteria are presented for component selection: feasibility, failure probability, consequences of failure, and benefits of the prognostics and health management (PHM) system. The basis and methods commonly used for prognostics algorithms are reviewed and summarized. Criteria for evaluating PHM architectures are presented: open, modular architecture; platform independence; graphical user interface for system development and/or results viewing; web-enabled tools; scalability; and standards compatibility. Thirteen software products were identified and discussed in the context of being potentially useful for deployment in a PHM program applied to systems in a nuclear power plant (NPP). These products were evaluated by using information available from company websites, product brochures, fact sheets, scholarly publications, and direct communication with vendors. The thirteen products were classified into four groups of software: research tools, PHM system development tools, deployable architectures, and peripheral tools. Eight software tools fell into the deployable architectures category. Of those eight, only two employ all six modules of a full PHM system. Five systems did not offer prognostic estimates, and one system employed the full health monitoring suite but lacked operations and maintenance support. Each product is briefly described in Appendix A, "Assessment Criteria." Selection of the most appropriate software package for a particular application will depend on the chosen component, system, or structure. Ongoing research will determine the most appropriate choices for a successful demonstration of PHM systems in aging NPPs.
Computation and Control IV, 1995
... 254 N. LYBECK AND K. BOWERS Proof of this theorem is given in [3]. Concatenating each side of... more ... 254 N. LYBECK AND K. BOWERS Proof of this theorem is given in [3]. Concatenating each side of the equation in step 3 yields The equation in step 5 ... Let the sinc error, assuming the last iterate is u2A/, be defined by < max l,(. r, y) es \\ ES\\= max< max| u (*, y)- where for p= 1, 2 ...
2006 IEEE Aerospace Conference
A fundamental problem in the development and validation of PHM technologies is the general shorta... more A fundamental problem in the development and validation of PHM technologies is the general shortage of realistic fault signature data. While healthy signatures can be obtained from operational systems, faults are relatively rare and difficult to observe. The PHM community must rely primarily on bench level seeded fault test data collected under a limited set of conditions. To augment physical data, a modeling and simulation toolset is being developed for the vibration signatures of faulted components in propulsion subsystems such as gearboxes. The toolset includes sophisticated dynamic models of vibration forcing for common rotating components such as bearings and gears based on detailed analysis of their physical interactions, including the effects of faults such as bearing spalling or gear tooth cracks. The response of the overall system, and thus the vibration signal seen at a particular sensor location can then be predicted using either FEA or a transfer function analysis of actual hardware. The purpose of the toolset is to leverage seeded fault test data (for example to study operating conditions or fault types that were not tested), improve fault diagnosability through optimal sensor placement, and enhance development, testing, and validation of diagnostic systems. Several examples are presented comparing simulated vibration signals to actual test data. 1,2 TABLE OF CONTENTS 1. INTRODUCTION .