An effective implementation of prognostic technology can re- duce costs and increase availability of assets. As a result of the rapidly growing interest in prognostics, researchers have independently developed a number of applications for asset-specific modeling and prediction. Consequently, there is some inconsistency in the understanding of key concepts for designing prognostic systems. This further complicates the already-challenging design of new prognostic systems. In order to progress from application-specific solutions towards structured and efficient prognostic implementations, the development of a comprehensive and pragmatic methodology is essential. Prognostic algorithm selection is a key activity to achieve consistency throughout the design process. In this paper we present a design decision framework which guides the designer towards a prognostic algorithm through a cause- effect flowchart. Failure modes, application characteristics, and qualitative and quantitative metrics are used to determine an appropriate approach for the stated problem. The application of the methodology can reduce the time and effort required to develop a prognostic system, ensure that all the possible design options have been considered, and provide a means to compare different prognostic algorithms consistently. The framework has been applied to different prognostic problems within the power industry to illuminate its effectiveness. Case studies are presented to show how the framework guides designers through the choice of prognostic algorithm according to system requirements. The results demonstrate the applicability of the methodology to the design of prognostic systems which consistently meet the established requirements.
How to Cite
requirements analysis, early system design, Generic prognostics methodology, model selection
An, D., Kim, N. H., & Choi, J.-H. (2015). Practical options for selecting data-driven or physics-based prognostics algorithms with reviews. Reliability Engineering & System Safety, 133(0), 223 - 236.
Aziz, N., Catterson, V., Judd, M., Rowland, S., & Bahadoors- ingh, S. (2014). Prognostic Modeling for Electrical Treeing in Solid Insulation using Pulse Sequence Analysis. In IEEE CEIDP 2014 (p. 373-376). DOI: 10.1109/CEIDP.2014.6995906
Baraldi, P., Compare, M., Sauco, S., & Zio, E. (2013). En- semble neural network-based particle filtering for prognostics. Mechanical Systems and Signal Processing, 41(12), 288 - 300.
Baraldi, P., Mangili, F., Gola, G., Nystad, B., & Zio, E. (2014). A Hybrid Ensemble-Based Approach for Process Parameter Estimation and Degradation Assess- ment in Offshore Oil Platforms. International Journal of Performability Engineering, 10(5), 497.
Baraldi, P., Mangili, F., & Zio, E. (2012, Dec). A Kalman Filter-Based Ensemble Approach With Application to Turbine Creep Prognostics. IEEE Transactions on Reliability, 61(4), 966-977. DOI: 10.1109/TR.2012.2221037
Borgonovo, E., & Apostolakis, G. (2001). A new importance measure for risk-informed decision making. Reliability Engineering & System Safety, 72(2), 193 - 212.
Catterson, V. (2014, Oct). Prognostic modeling of transformer aging using Bayesian particle filtering. In IEEE CEIDP 2014 (p. 413-416). DOI: 10.1109/CEIDP.2014.6995874
Coble, J. (2010). Merging Data Sources to Predict Remaining Useful Life — An Automated Method to Identify Prognostic Parameters (PhD Thesis). University of Tennessee.
Daigle, M., Saha, B., & Goebel, K. (2012, March). A Comparison of Filter-based Approaches for Model-based Prognostics. In IEEE Aerospace Conference 2012 (p. 1-10). doi: 10.1109/AERO.2012.6187363
Djurdjanovic, D., Lee, J., & Ni, J. (2003). Watchdog Agen- tan infotronics-based prognostics approach for product performance degradation assessment and prediction. Advanced Engineering Informatics, 17(34), 109 - 125. DOI: http://dx.doi.org/10.1016/j.aei.2004.07.005
Dodd, S. J. (2003). A deterministic model for the growth of non-conducting electrical tree structures. Journal of Physics D: Applied Physics, 36(2), 129.
Goebel, K., & Eklund, N. (2007). Prognostic Fusion for Uncertainty Reduction. In AIAA Infotech@Aerospace 2007 Conference and Exhibit. American Institute of Aeronautics and Astronautics. DOI: 10.2514/6.2007- 2843
Goebel, K., Saha, B., & Saxena, A. (2008). A comparison of three data-driven techniques for prognostics. In Machinery Failure Prevention Technology (MFPT) (pp. 119–131).
Gorjian, N., Ma, L., Mittinty, M., Yarlagadda, P., & Sun, Y. (2010). A review on reliability models with covariates. In Engineering Asset Lifecycle Management (p. 385- 397). Springer London. DOI: 10.1007/978-0-85729- 320-6 43
Govers, C. (1996). What and How About Quality Function Deployment (QFD). International Journal of Production Economics, 4647(0), 575 - 585.
Haykin, S. (1998). Neural Networks: A Comprehensive Foundation (2nd ed.). Upper Saddle River, NJ, USA: Prentice Hall PTR.
Hu, C., Youn, B. D., Wang, P., & Yoon, J. T. (2012). En- semble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering & System Safety, 103(0), 120 - 135.
Iamsumang, C., Mosleh, A., & Modarres, M. (2014, June). Computational Algorithm for Dynamic Hybrid Bayesian Network in On-line System Health Management applications. In
IEEE PHM 2014 (p. 1-8). DOI: 10.1109/ICPHM.2014.7036384
IEEE Power and Energy Society. (2011). IEEE Guide for Loading Mineral-Oil-Immersed Transformers and Step-Voltage Regulators. IEEE Std. C57.91.
ISO. (2004). Condition Monitoring and Diagnostics of Machines, Prognostics part 1: General Guidelines (Vol. ISO/IEC Directives Part 2; Tech. Rep. No. ISO13381- 1). International Organization for Standardization.
Khawaja, T., Vachtsevanos, G., & Wu, B. (2005, June). Reasoning About Uncertainty in Prognosis: A Confidence Prediction Neural Network Approach. In Fuzzy Information Processing Society (p. 7-12). DOI: 10.1109/NAFIPS.2005.1548498
Kumar, S., Torres, M., Chan, Y., & Pecht, M. (2008, June). A Hybrid Prognostics Methodology for Electronic Products. In IEEE IJCNN 2008 (p. 3479-3485). DOI: 10.1109/IJCNN.2008.4634294
Lam, J., Sankararaman, S., & Stewart, B. (2014). En- hanced Trajectory Based Similarity Prediction with Uncertainty Quantification. In PHM 2014.
Lee, J., Liao, L., Lapira, E., Ni, J., & Li, L. (2009). Informatics Platform for Designing and Deploying e- Manufacturing Systems. In Collaborative Design and
Planning for Digital Manufacturing (p. 1-35). Springer London. DOI: 10.1007/978-1-84882-287-0 1
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems – Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(12), 314 - 334.
Liao, L., & Kottig, F. (2014, March). Review of Hybrid Prognostics Approaches for Remaining Useful Life Prediction of Engineered Systems, and an Application to Battery Life Prediction. IEEE Transactions on Reliability, 63(1), 191-207. DOI: 10.1109/TR.2014.2299152
Ling, Y. (2013). Uncertainty quantification in time dependent reliability analysis (PhD Thesis). Vanderbilt University.
Liu, J., Djurdjanovic, D., Ni, J., Casoetto, N., & Lee, J. (2007). Similarity based method for manufacturing process performance prediction and diagnosis. Com- puters in Industry, 58(6), 558 - 566.
Liu, J., Vitelli, V., Seraoui, R., & Zio, E. (2014). Dynamic Weighted PSVR-Based Ensembles for Prognostics of Nuclear Components. In PHM Europe 2014.
Liu, Z., Li, Q., & Mu, C. (2012, Aug). A Hybrid LSSVR- HMM Based Prognostics Approach. In IHMSC 2012 (Vol. 2, p. 275-278). doi: 10.1109/IHMSC.2012.162
McGhee, M. J., Galloway, G., Catterson, V., Brown, B., & Harrison, E. (2014). Prognostic Modelling of Valve Degradation within Power Stations. In PHM 2014.
Nyanteh, Y., Graber, L., Edrington, C., Srivastava, S., & Cartes, D. (2011). Overview of Simulation Models for Partial Discharge and Electrical Treeing to Determine Feasibility for
Estimation of Remaining Life of Machine Insulation Systems. In EIC 2011 (p. 327-332). DOI: 10.1109/EIC.2011.5996172
Penha, R. L., & Hines, J. W. (2002). Hybrid System Modeling for Process Diagnostics. In Proceedings of the Maintenance and Reliability Conference - MARCON.
Peysson, F., Ouladsine, M., Outbib, R., Leger, J.-B., Myx, O., & Allemand, C. (2009, June). A Generic Prognostic Methodology Using Damage Trajectory Models. IEEE Transactions on Reliability, 58(2), 277-285. DOI: 10.1109/TR.2009.2020123
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning). The MIT Press.
Rodger, J. A. (2012). Toward reducing failure risk in an integrated vehicle health maintenance system: A fuzzy multi-sensor data fusion Kalman filter approach for IVHMS. Expert Systems with Applications, 39(10), 9821 - 9836.
Rudd, S., Catterson, V., McArthur, S., & Johnstone, C. (2011, July). Circuit Breaker Prognostics Using SF6 Data. In Power and Energy Society General Meeting, 2011 IEEE (p. 1-6). doi: 10.1109/PES.2011.6039599.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.