Sensor-Based Degradation Prediction and Prognostics for Remaining Useful Life Estimation: Validation on Experimental Data of Electric Motors
##plugins.themes.bootstrap3.article.main##
##plugins.themes.bootstrap3.article.sidebar##
Published
Nov 3, 2020
Federico Barbieri
J. Wesley Hines
Michael Sharp
Mauro Venturini
Abstract
Prognostics is an emerging science of predicting the health condition of a system and/or its components, based upon current and previous system status, with the ultimate goal of accurate prediction of the Remaining Useful Life (RUL). Based on this assumption, components/systems can be monitored to track the health state during operation. Acquired data are generally processed to extract relevant features related to the degradation condition of the component/system. Often, it is beneficial to combine several of these degradation parameters through an optimization process to develop a single parameter, called prognostic parameter, which can be trended to estimate the RUL. The approach adopted in this paper consists of a prognostic procedure which involves prognostic parameter generation and General Path Model (GPM) prediction. The Genetic Algorithm (GA) and Ordinary Least Squares (OLS) optimization methods will be used to develop suitable prognostic parameters from the selected features. Both time and frequency domain analysis will be investigated. Steady-state data obtained from electric motor accelerated degradation testing is used for method validation. Ten three-phase 5HP induction were run through temperature and humidity accelerated degradation cycles on a weekly basis. Of those, five presented similar degradation pathways due to bearing failure modes. The results show that the OLS method, on average, generated the best prognostic parameter performance using a combination of time domain features. However, the best single model performance was obtained using the GA methodology. In this case, the estimated RUL nearly coincided with the true RUL with an absolute percent error averaging under 5% near the end of life.
##plugins.themes.bootstrap3.article.details##
Keywords
Data-driven prognostics, motor prognostics
References
Bryg, D. J., Mink, G. & Jaw, L. C. (2008). Combining Lead Functions and Logistic Regression for Predicting Failures on an Aircraft Engine. ASME Paper No. GT2008-50118.
Byington, C.S., Watson, M., Edwards, D. & Stoelting, P. (2004). A Model-Based Approach to Prognostics and Health Management for Flight Control Actuators. Proceedings of the IEEE Aerospace Conference, 3551-3562.
Carden, E.P. & Fanning, P. (2004). Vibration Based Condition Monitoring: A Review. Structural Health Monitoring 3 (4): 355 - 377.
Carey, M.B. & Koenig, R.H. (1991). Reliability Assessment Based on Accelerated Degradation: A Case Study. IEEE Transactions on Reliability 40 (5) 1991: 499 - 506.
Carlin, B.P. & Louis, T.A. (2000), Bayes and Empirical Bayes Methods for Data Analysis. 2nd ed. Boca Raton: Chapman and Hall/CRC.
Cavarzere, A. & Venturini, M. (2012). Application of Forecasting Methodologies to Predict Gas Turbine Behavior Over Time. ASME J. Eng. Gas Turbines Power, 134(1), p. 012401.
Coble, J.B. (2010). Merging Data Sources to Predict Remaining Useful Life-An Automated Method to Identify Prognostic Parameters. A Doctoral Dissertation, The University of Tennessee, Knoxville TN.
Coble, J. & Hines J.W. (2011). Applying the General Path Model to Estimation of Remaining Useful Life. International Journal of Prognostics and Health Monitoring (IJPHM), Vol 2 (1) 007, pages: 13.
Coble, J. & Hines, J.W. (2012). Identifying Suitable Degradation Parameters for Individual-Based Prognostics in Diagnostics and Prognostics of Engineering Systems: Methods and Techniques, Editor, Seifedine Kadry, pp 135-150.
Dyer, D. & Stewart, R.M. (1978). Detection of rolling element bearing damage by statistical vibration analysis”, Journal of Mechanical design.
Elsayed, E.A. & Chen, A.C-K. (1998). Recent Research and Current Issues in Accelerated Testing. Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, 1998: 4704 - 4709.
Ferrell, B.L. (1999). JSF Prognostics and Health Management. Proceedings of the IEEE Aerospace Conference, 471.
Ferrell, B.L. (2000). Air Vehicle Prognostics and Health Management. Proceedings of the IEEE Aerospace Conference, 145 - 146.
Gelman, A., Carlin, J., Stern, H. & Rubin, D. (2004). Bayesian Data Analysis. 2nd ed. Boca Raton: Chapman and Hall/CRC.
Greitzer, F.L., Stahlman, E.J., Ferryman, T.A., Wilson, B.W., Kangas, L.J. & Sisk, D.R. (1999). Development of a Framework for Predicting Life of Mechanical Systems: Life Extension Analysisand Prognostics (LEAP). International Society of Logistics (SOLE) Symposium, Las Vegas, Aug 30 - Sept 2, 1999.
Gribok, A., J. Hines, A. Urmanov, & R. Uhrig. (2002). Regularization of Ill-Posed Surveillance and Diagnostic Measurements. Power Plant Surveillance and Diagnostics, eds. Da Ruan and P. Fantoni, Springer.
Gu, H., Zhao, J. & Zhang, X. (2013). Hybrid methodology of degradation feature extraction for bearing prognostics. Maintenance and Reliability; 15 (2): 195-201.
Heng, A., Zhang, S., Tan, A. C. C. & Mathew, J. (2009a). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23, pp. 724-739.
Heng, A., Tan, A. C. C., Mathew, J., Montgomery, N.,Banjevic, D. & Jardine, A. K. S. (2009b). Intelligent condition based prediction of machinery reliability. Mechanical Systems and Signal Processing, 23, pp. 1600-1614.
Hess, A., Calvello, G. & Frith, P. (2005). Challenges, Issues, and Lessons Learned Chasing the ‘Big P’: Real Predictive Prognostics Part 1. Proceedings of the IEEE Aerospace Conference, pp. 3610 - 3619.
Hines, J.W., Seibert, R. & Arndt, S.A. (2006a). Technical Review of On-Line Monitoring Techniques for Performance Assessment. (NUREG/CR-6895) Vol. 1, State-of-the-Art , Published January.
Hines, J.W., Usynin, A. & Urmanov, A. (2006b). Prognosis of Remaining Useful Life for Complex Engineering Systems. American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies (NPIC&HMIT), Albuquerue, NM.
Hines, J. W. (2009). Empirical Methods for Process and Equipment Prognostics. Reliability and Maintainability Symposium RAMS.Jardine, A. K. S., Lin, D. & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20, pp. 1483-1510.
Kacprzynski, G.J., Sarlashkar, A., Roemer, M.J., Hess, A. & Hardman, W. (2004). Predicting Remaining Life by Fusing the Physics of Failure Modeling with Diagnostics. Journal of the Mineral, Metals, and Materials Society 56 (3): 29 - 35.
Kalgren, P.W., Baybutt, M., Ginart, A., Minnella, C., Roemer, M.J. & Dabney, T. (2007). Application of Prognostic Health Management in Digital Electronic Systems. Proceedings of the 2007 IEEE Aerospace Conference, March: 1-9.
Keller, K., Swearingen, K., Sheahan, J., Baily, M., Dunsdon, J., Cleeve, B., Przytula, K.W. & Jordan, B. (2006). Aircraft Electrical Power Systems Prognostics and Health Management. IEEE Aerospace Conference, Big Sky, MT.
IEEE Standard 117. (1974). IEEE Standard Test Procedure for Evaluation of Systems of Insulating Materials for Random-Wound AC Electric Machinery Degradation and Testing Plan.
Li, R., Sopon, P. & He D. (2012). Fault features extraction for bearing prognostics. Springer Online Text.
Li, Y.G. & Nilkitsaranont, P. (2009). Gas turbine performance prognostic for condition-based maintenance. Applied Energy, 86, pp. 2152 – 2161.
Lindely, D.V. & Smith, A.F. (1972). Bayes Estimates for Linear Models. Journal of the Royal Statistical Society (B) 34 (1): 1-41.
Lipowsky, H., Staudacher, S., Bauer, M. & Schmidt, K. J. (2010). Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance. ASME J. Eng. Gas Turbines Power, 132 (3), p. 031602.
Loboda, I. & Feldshteiyn, Y. (2010). Polynomials and Neural Networks for Gas Turbine Monitoring. Proceedings of ASME Turbo Expo 2010. GT2010-23749.
Lu, C.J. & Meeker, W.Q. (1993). Using Degradation Measures to Estimate a Time-to-Failure Distribution. Technometrics Vol 35 No 2, pp 161-173.
Luo, J., Namburu, M., Pattipati, K., Qiao, L., Kawamoto M., & Chigusa, S. (2003). Model-based Prognostic Techniques. IEEE Systems Readiness Technology Conference (AUTOTESTCON), Sep 22-25: Anaheim, CA.
McInerny, S.,A. & Dai, Y. (2003). Basic Vibration Signal Processing for Bearing Fault Detection. IEEE Transactions on Education, Vol. 46, No. 1.
Mishra, S. & Pecht, M. (2002). In-situ Sensors for Product Reliability Monitoring. Proceedings of SPIE, vol 4755 pp 10-19.
Nam, A., Sharp, M., Hines, J. W. & Upadhyaya, B. R. (2012). Bayesian Methods for Successive Transitioning Between Prognostic Types: Lifecycle Prognostics. 8th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, San Diego, CA, pp. 1-8, Paper #262.
Orchard, M. & Vachtsevanos, G., (2007). A particle filtering approach for on-line failure prognosis in a planetary carrier plate. International Journal of Fuzzy Logic and Intelligent Systems, vol. 7, no. 4, pp. 221-227.
Orme, G. J. & Venturini, M. (2011). Property Risk Assessment for Power Plants: Methodology, Validation and Application. Energy, 36, pp. 3189-3203.
Palmè, T., Fast, M., Assadi, M. & Pike, A. (2009). Different Condition Monitoring Models for Gas Turbines by means of Artificial Neural Networks. Proceedings of ASME Turbo Expo 2009. GT2009-59364.
Park, C. & Padgett, W.J. (2006). Stochastic Degradation Models with Several Accelerating Variables. IEEE Transactions on Reliability 55 (2) 2006: 379 - 390.
Poyhonen, S., Jover, P. & Hyotyniemi, H. (2004). Signal processing of vibrations for condition monitoring of an induction motor. ISCCSP: 2004 First International Symposium on Control, Communications and Signal Processing, New York, pp. 499 - 502.
Puggina, N. & Venturini, M. (2012). Development of a Statistical Methodology for Gas Turbine Prognostics. ASME J. Eng. Gas Turbines Power, 134(2), 022401 (9 pages).
Ramakrishnan, A. & Pecht, M. (2003). A Life Consumption Monitoring Methodology for Electronic Systems. IEEE Transactions on Computer Packaging Technologies, Vol. 26, No. 3. pp.625-634.
Roemer, M.J., Dzakowic, J., Orsagh, R.F., Byington, C.S. & Vachtsevanos, G. (2005). Validation and Verification of Prognostic and Health Management Technologies. Proceedings of the IEEE Aerospace Conference, 3941 - 3947.
Saxena, A., Celaya, J., Saha, B., Saha, S. & Goebel, K. (2009a). On Applying the Prognostic Performance Metrics. Annual Conference of the Prognostics and Health Management Society 09, San Diego, CA, pp. 1-16, Paper #039.
Saxena, A., Celaya, J., Saha, B., Saha, S. & Goebel, K. (2009b). Evaluating Algorithm Performance Metrics Tailored for Prognostics. Proceedings of IEEE Aerospace Conference, Big Sky, MT.
Saxena, A, Celaya, J, Saha, B., Saha, S., & Goebel, K. (2010). Metrics for Offline, Evaluation of Prognostic Performance. International Journal of Prognostics and Health Management. Vol 1 (1) 001.
Sharp, M. (2012). Prognostic Approaches Using Transient Monitoring Methods. A Doctoral Dissertation, The University of Tennessee, Knoxville TN.
Sharp, M. (2013). Simple Metrics for Evaluating and Conveying Prognostic Model Performance To Users With Varied Backgrounds. International Journal of Prognostics and Health Management.
Si, X. S., Wang, W., Hua, C. H. & Zhou, D. H. (2011). Remaining useful life estimation - A review on the statistical data driven approaches. European Journal of Operational Research, 213, pp. 1-14.
SKF Group. Deep Groove Ball Bearing Catalog (Single Row). (2011). Catalog Number 6205-2Z.
Sreenuch, T., A. Tsourdos & I.K. Jennions (2013). Distributed embedded condition monitoring systems based on OSA-CBM standard. Computer Standards & Interfaces, 35 (2), pp 238–246.
Tandon N. & Choudhury A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32(8): 469-480.
Tang, L.C. & Chang, D.S. (1995). Reliability Prediction Using Nondestructive Accelerated Degradation Data: Case Study on Power Supplies. IEEE Transactions on Reliability 44 (4) 1995: 562-566.
Upadhyaya, B.R., Naghedolfeizi, M. & Raychaudhuri, B. (1994). Residual Life Estimation of Plant Components. P/PM Technology, p 22-29.
Upadhyaya, B.R., Erbay, A.S. & McClanahan J. P. (1997). Accelerated Aging Studies of Induction Motors and Fault Diagnostics. Maintenance and Reliability Center, The University of Tennessee, Knoxville.
Usynin, A., Hines, J.,W. & Urmanov, A. (2008), Uncertain Failure Thresholds in Cumulative Damage Models. Nuclear Engineering Department, University of Tennessee Knoxville.
Vachtsevanos, G., Kim, W., Al-Hasan, S., Rufus, F., Simon, M., Schrage, D. & Prasad, J.V.R. (1997). Mission Planning and Flight Control: Meeting the Challenge with Intelligent Techniques. Journal of Advanced Computational Intelligence, Vol. 1, No. 1, pp. 62-70.
Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part i: Quantitative model-based methods. Computers & Chemical Engineering, 27(3):293-311.
Venkatasubramanian, V., Rengaswamy, R. & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part ii: Qualitative models and search strategies. Computers & Chemical Engineering, 27(3):313-326.
Venkatasubramanian, V., Rengaswamy, R., Yin, K. & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part iii: Process history based methods. Computers & Chemical Engineering, 27(3):327-346.
Venturini, M. & Puggina, N. (2012). Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics. ASME J. Eng. Gas Turbines Power, 134(10), 101601 (9 pages).
Venturini, M. & Therkorn, D. (2013). Application of a Statistical Methodology for Gas Turbine Degradation Prognostics to Alstom Field Data. J. Eng. Gas Turbines Power 135(9), 091603 (10 pages), doi:10.1115/1.4024952.
Vichare, N. & Pecht, M. (2006). Prognostics and Health Management of Electronics. IEEE Transactions on Components and Packaging Technologies, 29 (1), pp 222 - 229.
Wang, P. & Vachtsevanos, G. (2001). Fault Prognostics Using Dynamic Wavelet Neural Networks. Journal of Artificial Intelligence for Engineering Design, Analysis and Manufacturing, vol.15, pp. 349-365.
Watson, M.J., Smith, M. J., Kloda, J., Byington, C. S. & Semega, K.. (2011). Prognostics and Health Management of Aircraft Engine EMA Systems. ASME Paper No. GT2011-46537.
Welz, Z., Nam, A, Sharp, M., Hines, J.W & Upadhyaya, B. R. (2014). Prognostics for Light Water Reactor Sustainability: Empirical Methods for Heat Exchanger Prognostic Lifetime Predictions. 2nd European Conference of the Prognostics and Health Management Society (PHME’14), Nantes, France, July 8-10.
Yang, H., Mathew, J. & Ma, L. (2003). Vibration Feauture Extraction Techniques for Fault Diagnosis of Rotating Machinery-A Literature Survey. Asia - Pacific Vibration Conference, 12-14 November, Gold Coast, Australia.
Zhang, X., Xu, R., Kwan, C., Liang, S.Y, Xie, Q. & Haynes, L. (2005). An Integrated Approach to Bearing Fault Diagnostics and Prognostics. American Control Conference June 8-10, 2005. Portland, OR, USA.
Byington, C.S., Watson, M., Edwards, D. & Stoelting, P. (2004). A Model-Based Approach to Prognostics and Health Management for Flight Control Actuators. Proceedings of the IEEE Aerospace Conference, 3551-3562.
Carden, E.P. & Fanning, P. (2004). Vibration Based Condition Monitoring: A Review. Structural Health Monitoring 3 (4): 355 - 377.
Carey, M.B. & Koenig, R.H. (1991). Reliability Assessment Based on Accelerated Degradation: A Case Study. IEEE Transactions on Reliability 40 (5) 1991: 499 - 506.
Carlin, B.P. & Louis, T.A. (2000), Bayes and Empirical Bayes Methods for Data Analysis. 2nd ed. Boca Raton: Chapman and Hall/CRC.
Cavarzere, A. & Venturini, M. (2012). Application of Forecasting Methodologies to Predict Gas Turbine Behavior Over Time. ASME J. Eng. Gas Turbines Power, 134(1), p. 012401.
Coble, J.B. (2010). Merging Data Sources to Predict Remaining Useful Life-An Automated Method to Identify Prognostic Parameters. A Doctoral Dissertation, The University of Tennessee, Knoxville TN.
Coble, J. & Hines J.W. (2011). Applying the General Path Model to Estimation of Remaining Useful Life. International Journal of Prognostics and Health Monitoring (IJPHM), Vol 2 (1) 007, pages: 13.
Coble, J. & Hines, J.W. (2012). Identifying Suitable Degradation Parameters for Individual-Based Prognostics in Diagnostics and Prognostics of Engineering Systems: Methods and Techniques, Editor, Seifedine Kadry, pp 135-150.
Dyer, D. & Stewart, R.M. (1978). Detection of rolling element bearing damage by statistical vibration analysis”, Journal of Mechanical design.
Elsayed, E.A. & Chen, A.C-K. (1998). Recent Research and Current Issues in Accelerated Testing. Proceedings of the IEEE Conference on Systems, Man, and Cybernetics, 1998: 4704 - 4709.
Ferrell, B.L. (1999). JSF Prognostics and Health Management. Proceedings of the IEEE Aerospace Conference, 471.
Ferrell, B.L. (2000). Air Vehicle Prognostics and Health Management. Proceedings of the IEEE Aerospace Conference, 145 - 146.
Gelman, A., Carlin, J., Stern, H. & Rubin, D. (2004). Bayesian Data Analysis. 2nd ed. Boca Raton: Chapman and Hall/CRC.
Greitzer, F.L., Stahlman, E.J., Ferryman, T.A., Wilson, B.W., Kangas, L.J. & Sisk, D.R. (1999). Development of a Framework for Predicting Life of Mechanical Systems: Life Extension Analysisand Prognostics (LEAP). International Society of Logistics (SOLE) Symposium, Las Vegas, Aug 30 - Sept 2, 1999.
Gribok, A., J. Hines, A. Urmanov, & R. Uhrig. (2002). Regularization of Ill-Posed Surveillance and Diagnostic Measurements. Power Plant Surveillance and Diagnostics, eds. Da Ruan and P. Fantoni, Springer.
Gu, H., Zhao, J. & Zhang, X. (2013). Hybrid methodology of degradation feature extraction for bearing prognostics. Maintenance and Reliability; 15 (2): 195-201.
Heng, A., Zhang, S., Tan, A. C. C. & Mathew, J. (2009a). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23, pp. 724-739.
Heng, A., Tan, A. C. C., Mathew, J., Montgomery, N.,Banjevic, D. & Jardine, A. K. S. (2009b). Intelligent condition based prediction of machinery reliability. Mechanical Systems and Signal Processing, 23, pp. 1600-1614.
Hess, A., Calvello, G. & Frith, P. (2005). Challenges, Issues, and Lessons Learned Chasing the ‘Big P’: Real Predictive Prognostics Part 1. Proceedings of the IEEE Aerospace Conference, pp. 3610 - 3619.
Hines, J.W., Seibert, R. & Arndt, S.A. (2006a). Technical Review of On-Line Monitoring Techniques for Performance Assessment. (NUREG/CR-6895) Vol. 1, State-of-the-Art , Published January.
Hines, J.W., Usynin, A. & Urmanov, A. (2006b). Prognosis of Remaining Useful Life for Complex Engineering Systems. American Nuclear Society International Topical Meeting on Nuclear Plant Instrumentation, Control, and Human-Machine Interface Technologies (NPIC&HMIT), Albuquerue, NM.
Hines, J. W. (2009). Empirical Methods for Process and Equipment Prognostics. Reliability and Maintainability Symposium RAMS.Jardine, A. K. S., Lin, D. & Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, 20, pp. 1483-1510.
Kacprzynski, G.J., Sarlashkar, A., Roemer, M.J., Hess, A. & Hardman, W. (2004). Predicting Remaining Life by Fusing the Physics of Failure Modeling with Diagnostics. Journal of the Mineral, Metals, and Materials Society 56 (3): 29 - 35.
Kalgren, P.W., Baybutt, M., Ginart, A., Minnella, C., Roemer, M.J. & Dabney, T. (2007). Application of Prognostic Health Management in Digital Electronic Systems. Proceedings of the 2007 IEEE Aerospace Conference, March: 1-9.
Keller, K., Swearingen, K., Sheahan, J., Baily, M., Dunsdon, J., Cleeve, B., Przytula, K.W. & Jordan, B. (2006). Aircraft Electrical Power Systems Prognostics and Health Management. IEEE Aerospace Conference, Big Sky, MT.
IEEE Standard 117. (1974). IEEE Standard Test Procedure for Evaluation of Systems of Insulating Materials for Random-Wound AC Electric Machinery Degradation and Testing Plan.
Li, R., Sopon, P. & He D. (2012). Fault features extraction for bearing prognostics. Springer Online Text.
Li, Y.G. & Nilkitsaranont, P. (2009). Gas turbine performance prognostic for condition-based maintenance. Applied Energy, 86, pp. 2152 – 2161.
Lindely, D.V. & Smith, A.F. (1972). Bayes Estimates for Linear Models. Journal of the Royal Statistical Society (B) 34 (1): 1-41.
Lipowsky, H., Staudacher, S., Bauer, M. & Schmidt, K. J. (2010). Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance. ASME J. Eng. Gas Turbines Power, 132 (3), p. 031602.
Loboda, I. & Feldshteiyn, Y. (2010). Polynomials and Neural Networks for Gas Turbine Monitoring. Proceedings of ASME Turbo Expo 2010. GT2010-23749.
Lu, C.J. & Meeker, W.Q. (1993). Using Degradation Measures to Estimate a Time-to-Failure Distribution. Technometrics Vol 35 No 2, pp 161-173.
Luo, J., Namburu, M., Pattipati, K., Qiao, L., Kawamoto M., & Chigusa, S. (2003). Model-based Prognostic Techniques. IEEE Systems Readiness Technology Conference (AUTOTESTCON), Sep 22-25: Anaheim, CA.
McInerny, S.,A. & Dai, Y. (2003). Basic Vibration Signal Processing for Bearing Fault Detection. IEEE Transactions on Education, Vol. 46, No. 1.
Mishra, S. & Pecht, M. (2002). In-situ Sensors for Product Reliability Monitoring. Proceedings of SPIE, vol 4755 pp 10-19.
Nam, A., Sharp, M., Hines, J. W. & Upadhyaya, B. R. (2012). Bayesian Methods for Successive Transitioning Between Prognostic Types: Lifecycle Prognostics. 8th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human Machine Interface Technologies, San Diego, CA, pp. 1-8, Paper #262.
Orchard, M. & Vachtsevanos, G., (2007). A particle filtering approach for on-line failure prognosis in a planetary carrier plate. International Journal of Fuzzy Logic and Intelligent Systems, vol. 7, no. 4, pp. 221-227.
Orme, G. J. & Venturini, M. (2011). Property Risk Assessment for Power Plants: Methodology, Validation and Application. Energy, 36, pp. 3189-3203.
Palmè, T., Fast, M., Assadi, M. & Pike, A. (2009). Different Condition Monitoring Models for Gas Turbines by means of Artificial Neural Networks. Proceedings of ASME Turbo Expo 2009. GT2009-59364.
Park, C. & Padgett, W.J. (2006). Stochastic Degradation Models with Several Accelerating Variables. IEEE Transactions on Reliability 55 (2) 2006: 379 - 390.
Poyhonen, S., Jover, P. & Hyotyniemi, H. (2004). Signal processing of vibrations for condition monitoring of an induction motor. ISCCSP: 2004 First International Symposium on Control, Communications and Signal Processing, New York, pp. 499 - 502.
Puggina, N. & Venturini, M. (2012). Development of a Statistical Methodology for Gas Turbine Prognostics. ASME J. Eng. Gas Turbines Power, 134(2), 022401 (9 pages).
Ramakrishnan, A. & Pecht, M. (2003). A Life Consumption Monitoring Methodology for Electronic Systems. IEEE Transactions on Computer Packaging Technologies, Vol. 26, No. 3. pp.625-634.
Roemer, M.J., Dzakowic, J., Orsagh, R.F., Byington, C.S. & Vachtsevanos, G. (2005). Validation and Verification of Prognostic and Health Management Technologies. Proceedings of the IEEE Aerospace Conference, 3941 - 3947.
Saxena, A., Celaya, J., Saha, B., Saha, S. & Goebel, K. (2009a). On Applying the Prognostic Performance Metrics. Annual Conference of the Prognostics and Health Management Society 09, San Diego, CA, pp. 1-16, Paper #039.
Saxena, A., Celaya, J., Saha, B., Saha, S. & Goebel, K. (2009b). Evaluating Algorithm Performance Metrics Tailored for Prognostics. Proceedings of IEEE Aerospace Conference, Big Sky, MT.
Saxena, A, Celaya, J, Saha, B., Saha, S., & Goebel, K. (2010). Metrics for Offline, Evaluation of Prognostic Performance. International Journal of Prognostics and Health Management. Vol 1 (1) 001.
Sharp, M. (2012). Prognostic Approaches Using Transient Monitoring Methods. A Doctoral Dissertation, The University of Tennessee, Knoxville TN.
Sharp, M. (2013). Simple Metrics for Evaluating and Conveying Prognostic Model Performance To Users With Varied Backgrounds. International Journal of Prognostics and Health Management.
Si, X. S., Wang, W., Hua, C. H. & Zhou, D. H. (2011). Remaining useful life estimation - A review on the statistical data driven approaches. European Journal of Operational Research, 213, pp. 1-14.
SKF Group. Deep Groove Ball Bearing Catalog (Single Row). (2011). Catalog Number 6205-2Z.
Sreenuch, T., A. Tsourdos & I.K. Jennions (2013). Distributed embedded condition monitoring systems based on OSA-CBM standard. Computer Standards & Interfaces, 35 (2), pp 238–246.
Tandon N. & Choudhury A. (1999). A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribology International, 32(8): 469-480.
Tang, L.C. & Chang, D.S. (1995). Reliability Prediction Using Nondestructive Accelerated Degradation Data: Case Study on Power Supplies. IEEE Transactions on Reliability 44 (4) 1995: 562-566.
Upadhyaya, B.R., Naghedolfeizi, M. & Raychaudhuri, B. (1994). Residual Life Estimation of Plant Components. P/PM Technology, p 22-29.
Upadhyaya, B.R., Erbay, A.S. & McClanahan J. P. (1997). Accelerated Aging Studies of Induction Motors and Fault Diagnostics. Maintenance and Reliability Center, The University of Tennessee, Knoxville.
Usynin, A., Hines, J.,W. & Urmanov, A. (2008), Uncertain Failure Thresholds in Cumulative Damage Models. Nuclear Engineering Department, University of Tennessee Knoxville.
Vachtsevanos, G., Kim, W., Al-Hasan, S., Rufus, F., Simon, M., Schrage, D. & Prasad, J.V.R. (1997). Mission Planning and Flight Control: Meeting the Challenge with Intelligent Techniques. Journal of Advanced Computational Intelligence, Vol. 1, No. 1, pp. 62-70.
Venkatasubramanian, V., Rengaswamy, R., Yin, K., & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part i: Quantitative model-based methods. Computers & Chemical Engineering, 27(3):293-311.
Venkatasubramanian, V., Rengaswamy, R. & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part ii: Qualitative models and search strategies. Computers & Chemical Engineering, 27(3):313-326.
Venkatasubramanian, V., Rengaswamy, R., Yin, K. & Kavuri, S. N. (2003). A review of process fault detection and diagnosis: Part iii: Process history based methods. Computers & Chemical Engineering, 27(3):327-346.
Venturini, M. & Puggina, N. (2012). Prediction Reliability of a Statistical Methodology for Gas Turbine Prognostics. ASME J. Eng. Gas Turbines Power, 134(10), 101601 (9 pages).
Venturini, M. & Therkorn, D. (2013). Application of a Statistical Methodology for Gas Turbine Degradation Prognostics to Alstom Field Data. J. Eng. Gas Turbines Power 135(9), 091603 (10 pages), doi:10.1115/1.4024952.
Vichare, N. & Pecht, M. (2006). Prognostics and Health Management of Electronics. IEEE Transactions on Components and Packaging Technologies, 29 (1), pp 222 - 229.
Wang, P. & Vachtsevanos, G. (2001). Fault Prognostics Using Dynamic Wavelet Neural Networks. Journal of Artificial Intelligence for Engineering Design, Analysis and Manufacturing, vol.15, pp. 349-365.
Watson, M.J., Smith, M. J., Kloda, J., Byington, C. S. & Semega, K.. (2011). Prognostics and Health Management of Aircraft Engine EMA Systems. ASME Paper No. GT2011-46537.
Welz, Z., Nam, A, Sharp, M., Hines, J.W & Upadhyaya, B. R. (2014). Prognostics for Light Water Reactor Sustainability: Empirical Methods for Heat Exchanger Prognostic Lifetime Predictions. 2nd European Conference of the Prognostics and Health Management Society (PHME’14), Nantes, France, July 8-10.
Yang, H., Mathew, J. & Ma, L. (2003). Vibration Feauture Extraction Techniques for Fault Diagnosis of Rotating Machinery-A Literature Survey. Asia - Pacific Vibration Conference, 12-14 November, Gold Coast, Australia.
Zhang, X., Xu, R., Kwan, C., Liang, S.Y, Xie, Q. & Haynes, L. (2005). An Integrated Approach to Bearing Fault Diagnostics and Prognostics. American Control Conference June 8-10, 2005. Portland, OR, USA.
Section
Technical Papers