Integrating Disparate Nuclear Data Sources for Improved Predictive Maintenance Modeling: Maintenance-Based Prognostics for Long-Term Equipment Operation

[1]  David W. Coit,et al.  A Method for Correlating Field Life Degradation with Reliability Prediction for Electronic Modules , 2005 .

[2]  M. Wulfsohn,et al.  Modeling the Relationship of Survival to Longitudinal Data Measured with Error. Applications to Survival and CD4 Counts in Patients with AIDS , 1995 .

[3]  R B Abernethy,et al.  Weibull Analysis Handbook , 1983 .

[4]  Saleh M. Al-Alawi,et al.  Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations , 2005, Environ. Model. Softw..

[5]  Marcantonio Catelani,et al.  Context awareness for maintenance decision making: A diagnosis and prognosis approach , 2015 .

[6]  Ilse Leal Aulenbacher,et al.  A data acquisition system for the Laguna Verde nuclear power plant , 2009 .

[7]  J. Wesley Hines,et al.  Current Computational Trends in Equipment Prognostics , 2008, Int. J. Comput. Intell. Syst..

[8]  Andrew K. S. Jardine,et al.  Optimizing a mine haul truck wheel motors’ condition monitoring program Use of proportional hazards modeling , 2001 .

[9]  Michael E. Sharp,et al.  Simple Metrics for Evaluating and Conveying Prognostic Model Performance To Users With Varied Backgrounds , 2013 .

[10]  Matthew Daigle,et al.  A Model-Based Prognostics Approach Applied to Pneumatic Valves , 2011 .

[11]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[12]  Liliane Pintelon,et al.  Integration of disparate data sources to perform maintenance prognosis and optimal decision making , 2012 .

[13]  Linxia Liao,et al.  Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction , 2014, IEEE Transactions on Reliability.

[14]  P. Baruah,et al.  HMMs for diagnostics and prognostics in machining processes , 2005 .

[15]  Belle R. Upadhyaya,et al.  Development and Validation of a Lifecycle-based Prognostics Architecture with Test Bed Validation , 2014 .

[16]  Jamie B. Coble,et al.  Merging Data Sources to Predict Remaining Useful Life – An Automated Method to Identify Prognostic Parameters , 2010 .

[17]  Abhinav Saxena,et al.  - 1-A COMPARISON OF THREE DATA-DRIVEN TECHNIQUES FOR PROGNOSTICS , 2008 .

[18]  Ryan M. Meyer,et al.  Lifecycle Prognostics Architecture for Selected High-Cost Active Components , 2011 .

[19]  Stephen V. Crowder,et al.  The Use of Degradation Measures to Design Reliability Test Plans. , 2014 .

[20]  B. Upadhyaya,et al.  Lifecycle Prognostic Algorithm Development and Application to Test Beds , 2013 .

[21]  James Prendergast,et al.  Implementation and benefits of introducing a computerised maintenance management system into a textile manufacturing company , 2004 .

[22]  Jamie B. Coble,et al.  Applying the General Path Model to Estimation of Remaining Useful Life , 2011, International Journal of Prognostics and Health Management.

[23]  Bhaskar Saha,et al.  Prognostics Methods for Battery Health Monitoring Using a Bayesian Framework , 2009, IEEE Transactions on Instrumentation and Measurement.

[24]  Erkki Jantunen,et al.  E-MAINTENANCE, A MEANS TO HIGH OVERALL EFFICIENCY , 2010 .

[25]  Belle R. Upadhyaya,et al.  On-Line Monitoring and Diagnostics of the Integrity of Nuclear Plant Steam Generators and Heat Exchangers , 2004 .

[26]  Benoît Iung,et al.  On the concept of e-maintenance: Review and current research , 2008, Reliab. Eng. Syst. Saf..

[27]  Lin Ma,et al.  Reducing maintenance cost through effective prediction analysis and process integration , 2006 .

[28]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[29]  E.R. Brown,et al.  Prognostics and Health Management A Data-Driven Approach to Supporting the F-35 Lightning II , 2007, 2007 IEEE Aerospace Conference.

[30]  Steven E. Rigdon Repairable Systems Reliability , 2014 .

[31]  Bruce P. Hallbert,et al.  Light Water Reactor Sustainability Program Advanced Instrumentation, Information, and Control Systems Technologies Technical Program Plan for FY 2016 , 2015 .

[32]  Luca Podofillini,et al.  Condition-based maintenance optimization by means of genetic algorithms and Monte Carlo simulation , 2002, Reliab. Eng. Syst. Saf..

[33]  C. Joseph Lu,et al.  Using Degradation Measures to Estimate a Time-to-Failure Distribution , 1993 .

[34]  Ahmad Mahir Razali,et al.  Mixture Weibull distributions for fitting failure times data , 2013, Appl. Math. Comput..

[35]  Srinivas Kumar Pinjala,et al.  An empirical investigation on the relationship between business and maintenance strategies , 2006 .

[36]  Uday Kumar,et al.  Hybrid Prognosis for Railway Health Assessment: an Information Fusion Approach for Phm Deployment , 2013 .

[37]  Tutpol Ardsomang,et al.  Heat Exchanger Fouling and Estimation of Remaining Useful Life , 2021 .

[38]  Abbas Barabadi Reliability model selection and validation using Weibull probability plot—A case study , 2013 .

[39]  Belle R. Upadhyaya,et al.  Lifecycle Prognostics: Transitioning between information types , 2015 .

[40]  Dana J. Vanier,et al.  Why industry needs asset management tools , 2001 .

[41]  Dawn An,et al.  Practical options for selecting data-driven or physics-based prognostics algorithms with reviews , 2015, Reliab. Eng. Syst. Saf..

[42]  K. Carroll,et al.  On the use and utility of the Weibull model in the analysis of survival data. , 2003, Controlled clinical trials.

[43]  Guang Meng,et al.  Updated proportional hazards model for equipment residual life prediction , 2011 .

[44]  Alan Y. Nam Bayesian-based Methods for Transitioning Between Prognostic Estimates to Leverage Available Data , 2016 .

[45]  Donald F. Specht,et al.  A general regression neural network , 1991, IEEE Trans. Neural Networks.

[46]  Kenneth Thomas Long-Term Instrumentation, Information, and Control Systems (II&C) Modernization Future Vision and Strategy , 2013 .

[47]  Belle R. Upadhyaya,et al.  Maintenance-based prognostics of nuclear plant equipment for long-term operation , 2017 .

[49]  Lin Li,et al.  Informatics platform for designing and deploying e-manufacturing systems , 2009 .

[50]  Sankalita Saha,et al.  Metrics for Offline Evaluation of Prognostic Performance , 2021, International Journal of Prognostics and Health Management.

[51]  Mark Voorhies Distance Metrics , 2017, Encyclopedia of Machine Learning and Data Mining.

[52]  Huairui Guo,et al.  Predicting remaining useful life of an individual unit using proportional hazards model and logistic regression model , 2006, RAMS '06. Annual Reliability and Maintainability Symposium, 2006..

[53]  Lifeng Xi,et al.  Condition monitoring system design with one-class and imbalanced-data classifier , 2009, 2009 16th International Conference on Industrial Engineering and Engineering Management.

[54]  R. W. Morris,et al.  The Wilcoxon rank sum test , 1976 .

[55]  Laura Swanson,et al.  An information-processing model of maintenance management , 2003 .

[56]  W. Weibull A Statistical Distribution Function of Wide Applicability , 1951 .

[57]  R. Keith Mobley President,et al.  Computer-Managed Maintenance Systems in Process Plants: A Step-by-Step Guide to Effective Management of Maintenance, Labor, and Inventory in Your Operation , 1998 .

[58]  R. Jovicic,et al.  Residual life estimation of a thermal power plant component: The high-pressure turbine housing case , 2009 .

[59]  MaCarmen Carnero An evaluation system of the setting up of predictive maintenance programmes , 2006 .

[60]  Krishna R. Pattipati,et al.  Model-based prognostic techniques [maintenance applications] , 2003, Proceedings AUTOTESTCON 2003. IEEE Systems Readiness Technology Conference..

[61]  D.,et al.  Regression Models and Life-Tables , 2022 .

[62]  J.W. Hines,et al.  Prognostic algorithm categorization with PHM Challenge application , 2008, 2008 International Conference on Prognostics and Health Management.