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DOE OSTI2020

Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study

BACKGROUND:We previously established a 53-gene prognostic signature for overall survival (OS) of gastric cancer patients. This retrospective multi-center study aimed to develop a clinically applicable gene expression detection assay and to investigate the prognostic value of this signature. METHODS:A TCGA gastric adenocarcinoma cohort (TCGA-STAD) was used for comparing 53-gene signature with other gene signatures. A high-throughput mRNA hybridization gene expression assay was developed to quantify the expression of 53-genes in formalin-fixed paraffin-embedded tissues of 540 patients enrolled from three hospitals. 180 patents were randomly selected from two hospitals to build a prognostic prediction model based on the 53-gene signature using leave-p-out (one-third out) cross-validation method together with Cox regression and Kaplan-Meier analysis, and the model was assessed on three validation cohorts. FINDINGS:In the evaluation phase, studies based on TCGA-STAD showed that the 53-gene signature was significantly superior to other three prognostic signatures and was independent of TCGA molecular subtypes and clinical factors. For clinical validation and utility, the prognostic scores were generated using the newly developed assay, which was reliable and sensitive, in 100 sampling training sets and were significantly associated with OS in 100 sampling validation sets. The scores were significantly associated with OS in three independent and combined validation cohorts, and in patients with stages II and III/IV. The multivariate Cox regression demonstrated that the prognostic power of the score was independent of clinical factors, consistent with those findings in the TCGA dataset. Finally, patients with good prognostic scores exhibited significantly a better 5-year OS rate from adjuvant FOLFOX chemotherapy after surgery than from other chemotherapies. INTERPRETATION:The 53-gene prognostic score system is clinically applicable for predicting the OS of patients independent of clinical factors in gastric cancers, which could also be a promising predictive biomarker for FOLFOX regimen. FUNDING:Chinese National Science and Technology, National Natural Science Foundation and Natural Science Foundation of Jiangsu Province.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2012

Prognostic Impact of the 6th and 7th American Joint Committee on Cancer TNM Staging Systems on Esophageal Cancer Patients Treated With Chemoradiotherapy

Purpose: The new 7th edition of the American Joint Committee on Cancer TNM staging system is based on pathologic data from esophageal cancers treated by surgery alone. There is no information available on evaluation of the new staging system with regard to prognosis of patients treated with chemoradiotherapy (CRT). The objective of this study was to evaluate the prognostic impact of the new staging system on esophageal cancer patients treated with CRT. Methods and Materials: A retrospective review was performed on 301 consecutive esophageal squamous cell carcinoma patients treated with CRT. Comparisons were made of the prognostic impacts of the 6th and 7th staging systems and the prognostic impacts of stage and prognostic groups, which were newly defined in the 7th edition. Results: There were significant differences between Stages I and III (p < 0.01) according to both editions. However, the 7th edition poorly distinguishes the prognoses of Stages III and IV (p = 0.36 by multivariate analysis) in comparison to the 6th edition (p = 0.08 by multivariate analysis), although these differences were not significant. For all patients, T, M, and gender were independent prognostic factors by multivariate analysis (p < 0.05). For the Stage I and II prognostic groups, survival curves showed a stepwise decrease with increase in stage, except for Stage IIA. However, there were no significant differences seen between each prognostic stage. Conclusions: Our study indicates there are several problems with the 7th TNM staging system regarding prognostic factors in patients undergoing CRT.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2016

Quantitative Analysis of {sup 18}F-Fluorodeoxyglucose Positron Emission Tomography Identifies Novel Prognostic Imaging Biomarkers in Locally Advanced Pancreatic Cancer Patients Treated With Stereotactic Body Radiation Therapy

Purpose: To identify prognostic biomarkers in pancreatic cancer using high-throughput quantitative image analysis. Methods and Materials: In this institutional review board–approved study, we retrospectively analyzed images and outcomes for 139 locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy (SBRT). The overall population was split into a training cohort (n=90) and a validation cohort (n=49) according to the time of treatment. We extracted quantitative imaging characteristics from pre-SBRT {sup 18}F-fluorodeoxyglucose positron emission tomography, including statistical, morphologic, and texture features. A Cox proportional hazard regression model was built to predict overall survival (OS) in the training cohort using 162 robust image features. To avoid over-fitting, we applied the elastic net to obtain a sparse set of image features, whose linear combination constitutes a prognostic imaging signature. Univariate and multivariate Cox regression analyses were used to evaluate the association with OS, and concordance index (CI) was used to evaluate the survival prediction accuracy. Results: The prognostic imaging signature included 7 features characterizing different tumor phenotypes, including shape, intensity, and texture. On the validation cohort, univariate analysis showed that this prognostic signature was significantly associated with OS (P=.002, hazard ratio 2.74), which improved upon conventional imaging predictors including tumor volume, maximum standardized uptake value, and total legion glycolysis (P=.018-.028, hazard ratio 1.51-1.57). On multivariate analysis, the proposed signature was the only significant prognostic index (P=.037, hazard ratio 3.72) when adjusted for conventional imaging and clinical factors (P=.123-.870, hazard ratio 0.53-1.30). In terms of CI, the proposed signature scored 0.66 and was significantly better than competing prognostic indices (CI 0.48-0.64, Wilcoxon rank sum test P<1e-6). Conclusion: Quantitative analysis identified novel {sup 18}F-fluorodeoxyglucose positron emission tomography image features that showed improved prognostic value over conventional imaging metrics. If validated in large, prospective cohorts, the new prognostic signature might be used to identify patients for individualized risk-adaptive therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

NASA NTRS2013

Distributed Prognostics and Health Management with a Wireless Network Architecture

A heterogeneous set of system components monitored by a varied suite of sensors and a particle-filtering (PF) framework, with the power and the flexibility to adapt to the different diagnostic and prognostic needs, has been developed. Both the diagnostic and prognostic tasks are formulated as a particle-filtering problem in order to explicitly represent and manage uncertainties in state estimation and remaining life estimation. Current state-of-the-art prognostic health management (PHM) systems are mostly centralized in nature, where all the processing is reliant on a single processor. This can lead to a loss in functionality in case of a crash of the central processor or monitor. Furthermore, with increases in the volume of sensor data as well as the complexity of algorithms, traditional centralized systems become for a number of reasons somewhat ungainly for successful deployment, and efficient distributed architectures can be more beneficial. The distributed health management architecture is comprised of a network of smart sensor devices. These devices monitor the health of various subsystems or modules. They perform diagnostics operations and trigger prognostics operations based on user-defined thresholds and rules. The sensor devices, called computing elements (CEs), consist of a sensor, or set of sensors, and a communication device (i.e., a wireless transceiver beside an embedded processing element). The CE runs in either a diagnostic or prognostic operating mode. The diagnostic mode is the default mode where a CE monitors a given subsystem or component through a low-weight diagnostic algorithm. If a CE detects a critical condition during monitoring, it raises a flag. Depending on availability of resources, a networked local cluster of CEs is formed that then carries out prognostics and fault mitigation by efficient distribution of the tasks. It should be noted that the CEs are expected not to suspend their previous tasks in the prognostic mode. When the prognostics task is over, and after appropriate actions have been taken, all CEs return to their original default configuration. Wireless technology-based implementation would ensure more flexibility in terms of sensor placement. It would also allow more sensors to be deployed because the overhead related to weights of wired systems is not present. Distributed architectures are furthermore generally robust with regard to recovery from node failures.

Goebel, Kai↗

DOE OSTI2011

Lifecycle Prognostics Architecture for Selected High-Cost Active Components

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: (1) component/system/structure selection, (2) prognostic algorithms, and (3) 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: (1) research tools, (2) PHM system development tools, (3) deployable architectures, and (4) 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. 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.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2012

New Breast Cancer Recursive Partitioning Analysis Prognostic Index in Patients With Newly Diagnosed Brain Metastases

Purpose: The aim of the study was to present a new breast cancer recursive partitioning analysis (RPA) prognostic index for patients with newly diagnosed brain metastases as a guide in clinical decision making. Methods and Materials: A prospectively collected group of 441 consecutive patients with breast cancer and brain metastases treated between the years 2003 and 2009 was assessed. Prognostic factors significant for univariate analysis were included into RPA. Results: Three prognostic classes of a new breast cancer RPA prognostic index were selected. The median survival of patients within prognostic Classes I, II, and III was 29, 9, and 2.4 months, respectively (p < 0.0001). Class I included patients with one or two brain metastases, without extracranial disease or with controlled extracranial disease, and with Karnofsky performance status (KPS) of 100. Class III included patients with multiple brain metastases with KPS of {<=}60. Class II included all other cases. Conclusions: The breast cancer RPA prognostic index is an easy and valuable tool for use in clinical practice. It can select patients who require aggressive treatment and those in whom whole-brain radiotherapy or symptomatic therapy is the most reasonable option. An individual approach is required for patients from prognostic Class II.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2012

Prognostic Indexes for Brain Metastases: Which Is the Most Powerful?

Purpose: The purpose of the present study was to compare the prognostic indexes (PIs) of patients with brain metastases (BMs) treated with whole brain radiotherapy (WBRT) using an artificial neural network. This analysis is important, because it evaluates the prognostic power of each PI to guide clinical decision-making and outcomes research. Methods and Materials: A retrospective prognostic study was conducted of 412 patients with BMs who underwent WBRT between April 1998 and March 2010. The eligibility criteria for patients included having undergone WBRT or WBRT plus neurosurgery. The data were analyzed using the artificial neural network. The input neural data consisted of all prognostic factors included in the 5 PIs (recursive partitioning analysis, graded prognostic assessment [GPA], basic score for BMs, Rotterdam score, and Germany score). The data set was randomly divided into 300 training and 112 testing examples for survival prediction. All 5 PIs were compared using our database of 412 patients with BMs. The sensibility of the 5 indexes to predict survival according to their input variables was determined statistically using receiver operating characteristic curves. The importance of each variable from each PI was subsequently evaluated. Results: The overall 1-, 2-, and 3-year survival rate was 22%, 10.2%, and 5.1%, respectively. All classes of PIs were significantly associated with survival (recursive partitioning analysis, P < .0001; GPA, P < .0001; basic score for BMs, P = .002; Rotterdam score, P = .001; and Germany score, P < .0001). Comparing the areas under the curves, the GPA was statistically most sensitive in predicting survival (GPA, 86%; recursive partitioning analysis, 81%; basic score for BMs, 79%; Rotterdam, 73%; and Germany score, 77%; P < .001). Among the variables included in each PI, the performance status and presence of extracranial metastases were the most important factors. Conclusion: A variety of prognostic models describe the survival of patients with BMs to a more or less satisfactory degree. Among the 5 PIs evaluated in the present study, GPA was the most powerful in predicting survival. Additional studies should include emerging biologic prognostic factors to improve the sensibility of these PIs.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

NASA NTRS2010

Distributed Prognostic Health Management with Gaussian Process Regression

Distributed prognostics architecture design is an enabling step for efficient implementation of health management systems. A major challenge encountered in such design is formulation of optimal distributed prognostics algorithms. In this paper. we present a distributed GPR based prognostics algorithm whose target platform is a wireless sensor network. In addition to challenges encountered in a distributed implementation, a wireless network poses constraints on communication patterns, thereby making the problem more challenging. The prognostics application that was used to demonstrate our new algorithms is battery prognostics. In order to present trade-offs within different prognostic approaches, we present comparison with the distributed implementation of a particle filter based prognostics for the same battery data.

Saha, Sankalita↗

DOE OSTI2012

Prognostic Significance of Human Apurinic/Apyrimidinic Endonuclease (APE/Ref-1) Expression in Rectal Cancer Treated With Preoperative Radiochemotherapy

Purpose: Human apurinic endonuclease/redox factor 1 (APE/Ref-1) mediates repair of radiation-induced DNA lesions and regulates transcription via redox-based activation. We investigated the predictive and prognostic significance of APE/Ref-1 expression in pretreatment biopsy specimens in locally advanced rectal cancer (LARC) (cT3-T4 or N+). Methods and Materials: APE/Ref-1 expression was analyzed by immunohistochemistry in pretreatment biopsy specimens obtained from 83 patients with LARC. Patients received preoperative radiotherapy of 50.4 Gy in 28 fractions, combined with oral capecitabine and leucovorin chemotherapy, followed by curative surgery. The prognostic significance of various clinicopathologic characteristics, including APE/Ref-1 protein expression, was evaluated. Results: APE/Ref-1 was expressed in 97% of patient samples. Exclusive APE/Ref-1 nuclear staining was observed in 49 of 83 samples (59%), and mixed nuclear and cytoplasmic staining was observed in 31 samples (37%). APE/Ref-1 nuclear expression levels were low in 49 patients (59%) and high in 34 patients (41%). The level of APE/Ref-1 nuclear expression was not a prognostic factor for overall and disease-free survival. Cytoplasmic expression of APE/Ref-1 was a borderline-significant predictive factor for pathologic tumor response (p = 0.08) and a significant prognostic factor for disease-free survival, as shown by univariate analysis (p = 0.037). Multivariate analysis confirmed that cytoplasmic localization of APE/Ref-1 is a significant predictor of disease-free survival (hazard ratio, 0.45; p = 0.046). Conclusions: APE/Ref-1 was expressed in a majority of pretreatment biopsy specimens from patients with LARC. The level of APE/Ref-1 nuclear expression was not a significant predictive and prognostic factor; however, cytoplasmic localization of the protein was negatively associated with disease-free survival. These results indicate that cytoplasmic expression of APE/Ref-1 represents an adverse prognostic factor for LARC patients who receive preoperative radiochemotherapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2014

Diagnostic and Prognostic Models for Generator Step-Up Transformers

In 2014, the online monitoring (OLM) of active components project under the Light Water Reactor Sustainability program at Idaho National Laboratory (INL) focused on diagnostic and prognostic capabilities for generator step-up transformers. INL worked with subject matter experts from the Electric Power Research Institute (EPRI) to augment and revise the GSU fault signatures previously implemented in the Electric Power Research Institute’s (EPRI’s) Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software. Two prognostic models were identified and implemented for GSUs in the FW-PHM Suite software. INL and EPRI demonstrated the use of prognostic capabilities for GSUs. The complete set of fault signatures developed for GSUs in the Asset Fault Signature Database of the FW-PHM Suite for GSUs is presented in this report. Two prognostic models are described for paper insulation: the Chendong model for degree of polymerization, and an IEEE model that uses a loading profile to calculates life consumption based on hot spot winding temperatures. Both models are life consumption models, which are examples of type II prognostic models. Use of the models in the FW-PHM Suite was successfully demonstrated at the 2014 August Utility Working Group Meeting, Idaho Falls, Idaho, to representatives from different utilities, EPRI, and the Halden Research Project.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2009

On Applying the Prognostic Performance Metrics

Prognostics performance evaluation has gained significant attention in the past few years. As prognostics technology matures and more sophisticated methods for prognostic uncertainty management are developed, a standardized methodology for performance evaluation becomes extremely important to guide improvement efforts in a constructive manner. This paper is in continuation of previous efforts where several new evaluation metrics tailored for prognostics were introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. Several shortcomings identified, while applying these metrics to a variety of real applications, are also summarized along with discussions that attempt to alleviate these problems. Further, these metrics have been enhanced to include the capability of incorporating probability distribution information from prognostic algorithms as opposed to evaluation based on point estimates only. Several methods have been suggested and guidelines have been provided to help choose one method over another based on probability distribution characteristics. These approaches also offer a convenient and intuitive visualization of algorithm performance with respect to some of these new metrics like prognostic horizon and alpha-lambda performance, and also quantify the corresponding performance while incorporating the uncertainty information.

Saxena, Abhinav↗

NASA NTRS2010

Metrics for Offline Evaluation of Prognostic Performance

Prognostic performance evaluation has gained significant attention in the past few years. Currently, prognostics concepts lack standard definitions and suffer from ambiguous and inconsistent interpretations. This lack of standards is in part due to the varied end-user requirements for different applications, time scales, available information, domain dynamics, etc. to name a few. The research community has used a variety of metrics largely based on convenience and their respective requirements. Very little attention has been focused on establishing a standardized approach to compare different efforts. This paper presents several new evaluation metrics tailored for prognostics that were recently introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics. Specifically, this paper presents a detailed discussion on how these metrics should be interpreted and used. These metrics have the capability of incorporating probabilistic uncertainty estimates from prognostic algorithms. In addition to quantitative assessment they also offer a comprehensive visual perspective that can be used in designing the prognostic system. Several methods are suggested to customize these metrics for different applications. Guidelines are provided to help choose one method over another based on distribution characteristics. Various issues faced by prognostics and its performance evaluation are discussed followed by a formal notational framework to help standardize subsequent developments.

Saxena, Abhinav↗

NASA NTRS2013

Vehicle Integrated Prognostic Reasoner (VIPR) Metric Report

This document outlines a set of metrics for evaluating the diagnostic and prognostic schemes developed for the Vehicle Integrated Prognostic Reasoner (VIPR), a system-level reasoner that encompasses the multiple levels of large, complex systems such as those for aircraft and spacecraft. VIPR health managers are organized hierarchically and operate together to derive diagnostic and prognostic inferences from symptoms and conditions reported by a set of diagnostic and prognostic monitors. For layered reasoners such as VIPR, the overall performance cannot be evaluated by metrics solely directed toward timely detection and accuracy of estimation of the faults in individual components. Among other factors, overall vehicle reasoner performance is governed by the effectiveness of the communication schemes between monitors and reasoners in the architecture, and the ability to propagate and fuse relevant information to make accurate, consistent, and timely predictions at different levels of the reasoner hierarchy. We outline an extended set of diagnostic and prognostics metrics that can be broadly categorized as evaluation measures for diagnostic coverage, prognostic coverage, accuracy of inferences, latency in making inferences, computational cost, and sensitivity to different fault and degradation conditions. We report metrics from Monte Carlo experiments using two variations of an aircraft reference model that supported both flat and hierarchical reasoning.

Cornhill, Dennis↗

NASA NTRS2017

A Virtual Laboratory for Aviation and Airspace Prognostics Research

Integration of Unmanned Aerial Vehicles (UAVs), autonomy, spacecraft, and other aviation technologies, in the airspace is becoming more and more complicated, and will continue to do so in the future. Inclusion of new technology and complexity into the airspace increases the importance and difficulty of safety assurance. Additionally, testing new technologies on complex aviation systems and systems of systems can be challenging, expensive, and at times unsafe when implementing real life scenarios. The application of prognostics to aviation and airspace management may produce new tools and insight into these problems. Prognostic methodology provides an estimate of the health and risks of a component, vehicle, or airspace and knowledge of how that will change over time. That measure is especially useful in safety determination, mission planning, and maintenance scheduling. In our research, we develop a live, distributed, hardware- in-the-loop Prognostics Virtual Laboratory testbed for aviation and airspace prognostics. The developed testbed will be used to validate prediction algorithms for the real-time safety monitoring of the National Airspace System (NAS) and the prediction of unsafe events. In our earlier work1 we discussed the initial Prognostics Virtual Laboratory testbed development work and related results for milestones 1 & 2. This paper describes the design, development, and testing of the integrated tested which are part of milestone 3, along with our next steps for validation of this work. Through a framework consisting of software/hardware modules and associated interface clients, the distributed testbed enables safe, accurate, and inexpensive experimentation and research into airspace and vehicle prognosis that would not have been possible otherwise. The testbed modules can be used cohesively to construct complex and relevant airspace scenarios for research. Four modules are key to this research: the virtual aircraft module which uses the X-Plane simulator and X-PlaneConnect toolbox, the live aircraft module which connects fielded aircraft using onboard cellular communications devices, the hardware in the loop (HITL) module which connects laboratory based bench-top hardware testbeds and the research module which contains diagnostics and prognostics tools for analysis of live air traffic situations and vehicle health conditions. The testbed also features other modules for data recording and playback, information visualization, and air traffic generation. Software reliability, safety, and latency are some of the critical design considerations in development of the testbed.

LVC-DE↗

NASA NTRS2018

A Fast Monte Carlo Method for Model-Based Prognostics Based on Stochastic Calculus

This work proposes a fast Monte Carlo method to solve differential equations utilized in model-based prognostics. The methodology is derived from the theory of stochastic calculus, and the goal of such a method is to speed up the estimation of the probability density functions describing the independent variable evolution over time. In the prognostic scenarios presented in this paper, the stochastic differential equations describe variables directly or indirectly related to the degradation of a monitored system. The method allows the estimation of the probability density functions by solving the deterministic equation and approximating the stochastic integrals using samples of the model noise. By so doing, the prognostic problem is solved without the Monte Carlo simulation based on Euler's forward method, which is typically the most time consuming task of the prediction stage. Three different prognostic scenarios are presented as proof of concept: (i) life prediction of electrolytic capacitors, (ii) remaining time to discharge of Lithium-ion batteries, and (iii) prognostic of cracked structures under fatigue loading. The paper shows how the method produces probability density functions that are statistically indistinguishable from the distributions estimated with Euler's forward Monte Carlo simulation. However, the proposed solution is orders of magnitude faster when computing the time-to-failure distribution of the monitored system. The approach may enable complex real-time prognostics and health management solutions with limited computing power.

Corbetta, M.↗

NASA NTRS2019

A Fast Monte Carlo Method for Model-Based Prognostics Based on Stochastic Calculus

stochastic calculus↗

DOE OSTI2015

Prognostic and health management of active assets in nuclear power plants

This study presents the development of diagnostic and prognostic capabilities for active assets in nuclear power plants (NPPs). The research was performed under the Advanced Instrumentation, Information, and Control Technologies Pathway of the Light Water Reactor Sustainability Program. Idaho National Laboratory researched, developed, implemented, and demonstrated diagnostic and prognostic models for generator step-up transformers (GSUs). The Fleet-Wide Prognostic and Health Management (FW-PHM) Suite software developed by the Electric Power Research Institute was used to perform diagnosis and prognosis. As part of the research activity, Idaho National Laboratory implemented 22 GSU diagnostic models in the Asset Fault Signature Database and two wellestablished GSU prognostic models for the paper winding insulation in the Remaining Useful Life Database of the FW-PHM Suite. The implemented models along with a simulated fault data stream were used to evaluate the diagnostic and prognostic capabilities of the FW-PHM Suite. Knowledge of the operating condition of plant asset gained from diagnosis and prognosis is critical for the safe, productive, and economical long-term operation of the current fleet of NPPs. This research addresses some of the gaps in the current state of technology development and enables effective application of diagnostics and prognostics to nuclear plant assets.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2015

Progress Towards Prognostic Health Management of Passive Components in Advanced Reactors – Model Selection and Evaluation

This paper presents recent progress towards developing a prognostic health management framework for passive components of advanced reactors (AR). The focus of this paper is on lifecycle prognostics for passive components using a Bayesian prognostic algorithm that provides a natural framework for incorporating different sources of variability, and uncertainties inherent in the operations of AR. High-temperature creep damage, a prototypic failure mechanism in AR materials, is used as the context for this research. A Bayesian model selection approach is implemented to select the appropriate creep degradation model at any given time, using relevant sensor measurements reflecting the material degradation state. The model selection approach, based on reversible jump Markov chain Monte Carlo methods, is integrated with Bayesian particle filter-based prognostic framework. The proposed approach is evaluated using strain measurements obtained from accelerated creep testing of stainless steel specimens. Results indicate feasibility of the proposed approach in accurately identifying the creep degradation stage from the available measurements at a given time. Effect of uncertainties in material degradation model and measurement noise on the performance of the prognostic algorithm is also investigated.

advanced reactor prognostics↗