Jamie B. Coble
University of Tennessee
Network
Latest external collaboration on country level. Dive into details by clicking on the dots.
Publication
Featured researches published by Jamie B. Coble.
ieee conference on prognostics and health management | 2008
Jamie B. Coble; J.W. Hines
Prognostic algorithms can be divided into three major categories. The most basic methods model the component or system reliability using failure time data and conventional models such as the Weibull. When information pertaining to the operating condition and environmental stressors are available, stress-based techniques can be used. The third type of prognostics is termed effects-based. It is truly an individual based prognostics because it uses information as to how the individual component is affected by the usage condition. This paper presents a summary of the three prognostic types and describes the ongoing development of a Matlab-based set of tools to facilitate prognostic model development. The application of models of each type is illustrated with the PHM Challenge data set. The paper shows the advantages of identifying a degradation parameter to provide for the use of effects-based prognostics.
Journal of Chromatography A | 2014
Jamie B. Coble; Carlos G. Fraga
Preprocessing software, which converts large instrumental data sets into a manageable format for data analysis, is crucial for the discovery of chemical signatures in metabolomics, chemical forensics, and other signature-focused disciplines. Here, four freely available and published preprocessing tools known as MetAlign, MZmine, SpectConnect, and XCMS were evaluated for impurity profiling using nominal mass GC/MS data and accurate mass LC/MS data. Both data sets were previously collected from the analysis of replicate samples from multiple stocks of a nerve-agent precursor and method blanks. Parameters were optimized for each of the four tools for the untargeted detection, matching, and cataloging of chromatographic peaks from impurities present in the stock samples. The peak table generated by each preprocessing tool was analyzed to determine the number of impurity components detected in all replicate samples per stock and absent in the method blanks. A cumulative set of impurity components was then generated using all available peak tables and used as a reference to calculate the percent of component detections for each tool, in which 100% indicated the detection of every known component present in a stock. For the nominal mass GC/MS data, MetAlign had the most component detections followed by MZmine, SpectConnect, and XCMS with detection percentages of 83, 60, 47, and 41%, respectively. For the accurate mass LC/MS data, the order was MetAlign, XCMS, and MZmine with detection percentages of 80, 45, and 35%, respectively. SpectConnect did not function for the accurate mass LC/MS data. Larger detection percentages were obtained by combining the top performer with at least one of the other tools such as 96% by combining MetAlign with MZmine for the GC/MS data and 93% by combining MetAlign with XCMS for the LC/MS data. In terms of quantitative performance, the reported peak intensities from each tool had averaged absolute biases (relative to peak intensities obtained using instrument software) of 41, 4.4, 1.3 and 1.3% for SpectConnect, MetAlign, XCMS, and MZmine, respectively, for the GC/MS data. For the LC/MS data, the averaged absolute biases were 22, 4.5, and 3.1% for MetAlign, MZmine, and XCMS, respectively. In summary, MetAlign performed the best in terms of the number of component detections; however, more than one preprocessing tool should be considered to avoid missing impurities or other trace components as potential chemical signatures.
ieee international conference on technologies for homeland security | 2013
Pradeep Ramuhalli; Mahantesh Halappanavar; Jamie B. Coble; Mukul Dixit
Effective reconstitution approaches for cyber systems are needed to keep critical infrastructure operational in the face of an intelligent adversary. The reconstitution response, including recovery and adaptation, may require significant reconfiguration of the system at all levels to render the cyber-system resilient to ongoing and future attacks or faults while maintaining continuity of operations. A theoretical basis for optimal dynamic reconstitution is needed to address the challenge of ensuring that dynamic reconstitution is optimal with respect to resilience metrics, and is being developed and evaluated in this project. Such a framework provides the technical basis for evaluating cyber-defense and reconstitution approaches. This paper describes a preliminary framework that may be used to develop and evaluate concepts for effective autonomous reconstitution of compromised cyber systems.
Archive | 2012
Kevin L. Simmons; Pradeep Ramuhalli; David L. Brenchley; Jamie B. Coble; Hash Hashemian; Robert Konnik; Sheila Ray
The purpose of the non-destructive evaluation (NDE) R&D Roadmap for Cables is to support the Materials Aging and Degradation (MAaD) R&D pathway. The focus of the workshop was to identify the technical gaps in detecting aging cables and predicting their remaining life expectancy. The workshop was held in Knoxville, Tennessee, on July 30, 2012, at Analysis and Measurement Services Corporation (AMS) headquarters. The workshop was attended by 30 experts in materials, electrical engineering, U.S. Nuclear Regulatory Commission (NRC), U.S. Department of Energy (DOE) National Laboratories (Oak Ridge National Laboratory, Pacific Northwest National Laboratory, Argonne National Laboratory, and Idaho National Engineering Laboratory), NDE instrumentation development, universities, commercial NDE services and cable manufacturers, and Electric Power Research Institute (EPRI). The motivation for the R&D roadmap comes from the need to address the aging management of in-containment cables at nuclear power plants (NPPs).
2012 Future of Instrumentation International Workshop (FIIW) Proceedings | 2012
Pradeep Ramuhalli; Jamie B. Coble; Ryan M. Meyer; Leonard J. Bond
There is growing interest in longer-term operation of the current US nuclear power plant (NPP) fleet. This paper presents an overview of prognostic health management (PHM) technologies that could play a role in the safe and effective operation of nuclear power plants during extended life. A case study in prognostics for materials degradation assessment, using laboratory-scale measurements, is briefly discussed, and technical gaps that need to be addressed prior to PHM system deployment for nuclear power life extension are presented.
Nuclear Engineering and Technology | 2014
Jamie B. Coble; J. Wesley Hines
The general path model (GPM) is one approach for performing degradation-based, or Type III, prognostics. The GPM fits a parametric function to the collected observations of a prognostic parameter and extrapolates the fit to a failure threshold. This approach has been successfully applied to a variety of systems when a sufficient number of prognostic parameter observations are available. However, the parametric fit can suffer significantly when few data are available or the data are very noisy. In these instances, it is beneficial to include additional information to influence the fit to conform to a prior belief about the evolution of system degradation. Bayesian statistical approaches have been proposed to include prior information in the form of distributions of expected model parameters. This requires a number of run-to-failure cases with tracked prognostic parameters; these data may not be readily available for many systems. Reliability information and stressor-based (Type I and Type II, respectively) prognostic estimates can provide the necessary prior belief for the GPM. This article presents the Bayesian updating framework to include prior information in the GPM and compares the efficacy of including different information sources on two data sets.
Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability | 2015
Michael Sharp; Jamie B. Coble; Alan Nam; J. Wes Hines; Belle R. Upadhyaya
As nuclear power plants seek to extend their licenses and maintain a high level of performance and safety, online monitoring and assessment of system degradation are becoming a crucial consideration. A goal of the DOE Light Water Reactor Sustainability program is the accurate estimation of the remaining useful life of nuclear power plant systems, structures, and components. Effective prognostic systems should seamlessly predict the remaining useful life from beginning of component life to end of component life, so-called Lifecycle Prognostics. When a component is first put into operation, the only information available may be past failure times of similar components or the expected distribution of failure times derived from reliability analyses of these data (Type I Prognostics). These data provide an estimated life for the average component operating under average usage conditions. As the component operates, it begins to consume its available life at a rate largely influenced by the system and environmental stresses. Information from these recorded stresses can be used to update the expected failure time distribution (Type II Prognostics). The incorporation of stressor information allows for the estimation of the remaining useful life for an average component operating under specific usage conditions. After continued operations, measurable levels of degradation may evolve, which allows for further improvement of the failure time distribution estimate by incorporating these health indicators (Type III Prognostics). This article presents a framework for using Bayesian methods to transition between prognostic model types and to update failure time distribution estimates as new information becomes available.
ieee conference on prognostics and health management | 2013
Ryan M. Meyer; Jamie B. Coble; Pradeep Ramuhalli
Advanced small modular reactors (aSMRs), which are based on modularization of advanced reactor concepts, may provide a longer-term alternative to traditional light-water reactors and near term small modular reactors (SMRs), which are based on integral pressurized water reactor (iPWR) concepts. aSMRs are conceived for applications in remote locations and for diverse missions that include providing process or district heating, water desalination, and hydrogen production. Several challenges exist with respect to cost-effective operations and maintenance (O&M) of aSMRs, including the impacts of aggressive operating environments and modularity, and limiting these costs and staffing needs will be essential to ensuring the economic feasibility of aSMR deployment. In this regard, prognostic health management (PHM) systems have the potential to play a vital role in supporting the deployment of aSMR systems. This paper identifies requirements and technical gaps associated with implementation of PHM systems for passive aSMR components.
Archive | 2013
Pradeep Ramuhalli; Guang Lin; Susan L. Crawford; Bledar A. Konomi; Jamie B. Coble; Brent Shumaker; Hash Hashemian
This report describes research towards the development of advanced algorithms for online calibration monitoring. The objective of this research is to develop the next generation of online monitoring technologies for sensor calibration interval extension and signal validation in operating and new reactors. These advances are expected to improve the safety and reliability of current and planned nuclear power systems as a result of higher accuracies and increased reliability of sensors used to monitor key parameters. The focus of this report is on documenting the outcomes of the first phase of R&D under this project, which addressed approaches to uncertainty quantification (UQ) in online monitoring that are data-driven, and can therefore adjust estimates of uncertainty as measurement conditions change. Such data-driven approaches to UQ are necessary to address changing plant conditions, for example, as nuclear power plants experience transients, or as next-generation small modular reactors (SMR) operate in load-following conditions.
2012 Future of Instrumentation International Workshop (FIIW) Proceedings | 2012
Jamie B. Coble; Pradeep Ramuhalli; Ryan M. Meyer; Hash Hashemian; Brent Shumaker; Dara Cummins
Currently in the United States, periodic sensor recalibration is required for all safety-related sensors, typically occurring at every refueling outage; and it has emerged as a critical path item for shortening outage duration in some plants. International application of calibration monitoring has shown that sensors may operate for longer periods within calibration tolerances. This issue is expected to also be important as the United States looks to the next generation of reactor designs (such as small modular reactors and advanced reactor concepts), given the anticipated longer refueling cycles, proposed advanced sensors, and digital instrumentation and control systems. Online monitoring (OLM) can be employed to identify those sensors that require calibration, allowing for calibration of only those sensors that need it. The U.S. Nuclear Regulatory Commission (NRC) accepted the general concept of OLM for sensor calibration monitoring in 2000, but no U.S. plants have been granted the necessary license amendment to apply it. This paper summarizes a recent state-of-the-art assessment of online calibration monitoring in the nuclear power industry, including sensors, calibration practice, and OLM algorithms. This assessment identifies key research needs and gaps that prohibit integration of the NRC-approved online calibration monitoring system in the U.S. nuclear power industry. Several technical needs were identified, including an understanding of the impacts of sensor degradation on measurements for both conventional and emerging sensors; the quantification of uncertainty in online calibration assessment; determination of calibration acceptance criteria and quantification of the effect of acceptance criteria variability on system performance; and assessment of the feasibility of using virtual sensor estimates to replace identified faulty sensors in order to extend operation to the next convenient maintenance opportunity.