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Erschienen in:
Basic Concepts of Reliability Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

9. Case Studies: Prognostics and Health Management (PHM)

verfasst von : Chao Hu, Byeng D. Youn, Pingfeng Wang

Erschienen in: Engineering Design under Uncertainty and Health Prognostics

Verlag: Springer International Publishing

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Abstract

Prognostics and health management (PHM) technology has been successfully implemented into engineering practice in diverse settings. This chapter presents case studies that explain successful PHM practices in several engineering applications: (1) steam turbine rotors, (2) wind turbine gearboxes, (3) the core and windings in power transformers, (4) power generator stator windings, (5) lithium-ion batteries, (6) fuel cells, and (7) water pipelines. These examples provide useful findings about the four core functions of PHM technology, contemporary technology trends, and industrial values.

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Vorheriges Kapitel Time-Dependent Reliability Analysis in Operation: Prognostics and Health Management
Literatur
1.
Gupta, K. (1997). Vibration—A tool for machine diagnostics and condition monitoring. Sadhana, 22, 393–410.CrossRef
2.
Lin, J., & Qu, L. S. (2000). Feature extraction based on Morlet wavelet and its application for mechanical fault diagnosis. Journal of Sound and Vibration, 234, 135–148.CrossRef
3.
Jeon, B., Jung, J., Youn, B., Kim, Y.-W., & Bae, Y.-C. (2015). Datum unit optimization for robustness of a journal bearing diagnosis system. International Journal of Precision Engineering and Manufacturing, 16, 2411–2425.CrossRef
4.
Bonnardot, F., El Badaoui, M., Randall, R., Daniere, J., & Guillet, F. (2005). Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation). Mechanical Systems and Signal Processing, 19, 766–785.CrossRef
5.
Villa, L. F., Reñones, A., Perán, J. R., & de Miguel, L. J. (2011). Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation. Mechanical Systems and Signal Processing, 25, 2157–2168, 8.CrossRef
6.
Han, T., Yang, B.-S., Choi, W.-H., & Kim, J.-S. (2006). Fault diagnosis system of induction motors based on neural network and genetic algorithm using stator current signals. International Journal of Rotating Machinery, 2006.
7.
Yang, B.-S., & Kim, K. J. (2006). Application of Dempster-Shafer theory in fault diagnosis of induction motors using vibration and current signals. Mechanical Systems and Signal Processing, 20, 403–420.CrossRef
8.
Jung, J.H., Jeon, B.C., Youn, B.D., Kim, M., Kim, D. & Kim, Y., (2017). Omnidirectional regeneration (ODR) of proximity sensor signals for robust diagnosis of journal bearing systems. Mechanical Systems and Signal Processing, 90, 189–207.CrossRef
9.
Guo, B., Damper, R. I., Gunn, S. R., & Nelson, J. D. B. (2008). A fast separability-based feature-selection method for high-dimensional remotely sensed image classification. Pattern Recognition, 41, 1653–1662, 5.CrossRef
10.
Abbasion, S., Rafsanjani, A., Farshidianfar, A., & Irani, N. (2007). Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mechanical Systems and Signal Processing, 21, 2933–2945.CrossRef
11.
Widodo, A., & Yang, B.-S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21, 2560–2574.CrossRef
12.
Heng, A., Zhang, S., Tan, A. C. C., & Mathew, J. (2009). Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing, 23, 724–739, 4.CrossRef
13.
Zaher, A., McArthur, S., Infield, D., & Patel, Y. (2009). Online wind turbine fault detection through automated SCADA data analysis. Wind Energy, 12, 574–593.CrossRef
14.
Tchakoua, P., Wamkeue, R., Ouhrouche, M., Slaoui-Hasnaoui, F., Tameghe, T. A., & Ekemb, G. (2014). Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies, 7, 2595–2630.CrossRef
15.
Samuel, P. D., Conroy, J. K., & Pines, D. J. (2004). Planetary transmission diagnostics. NASA CR, 213068.
16.
Lebold, M., McClintic, K., Campbell, R., Byington, C., & Maynard, K. (2000). Review of vibration analysis methods for gearbox diagnostics and prognostics. In Proceedings of the 54th Meeting of the Society for Machinery Failure Prevention Technology (p. 16).
17.
Lau, B. C. P., Ma, E. W. M., & Pecht, M. (2012). Review of offshore wind turbine failures and fault prognostic methods. In 2012 IEEE Conference on Prognostics and System Health Management (PHM) (pp. 1–5).
18.
Feldman, K., Jazouli, T., & Sandborn, P. A. (2009). A methodology for determining the return on investment associated with prognostics and health management. IEEE Transactions on Reliability, 58, 305–316.CrossRef
19.
Nilsson, J., & Bertling, L. (2007). Maintenance management of wind power systems using condition monitoring systems—Life cycle cost analysis for two case studies. IEEE Transactions on Energy Conversion, 22, 223–229.CrossRef
20.
Hu, C., Wang, P., Youn, B. D., Lee, W.-R., & Yoon, J. T. (2012). Copula-based statistical health grade system against mechanical faults of power transformers. IEEE Transactions on Power Delivery, 27, 1809–1819.CrossRef
21.
Shengchang, J., Yongfen, L., & Yanming, L. (2006). Research on extraction technique of transformer core fundamental frequency vibration based on OLCM. IEEE Transactions on Power Delivery, 21, 1981–1988.CrossRef
22.
Wang, P., Youn, B. D., Hu, C., Ha, J. M., & Jeon, B. (2014). A probabilistic detectability-based sensor network design method for system health monitoring and prognostics. Journal of Intelligent Material Systems and Structures, 1045389X14541496.
23.
Hu, C., Youn, B. D., Wang, P., & Yoon, J. T. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering & System Safety, 103, 120–135.CrossRef
24.
Inoue, Y., Hasegawa, H., Sekito, S., Sotodate, M., Shimada, H., & Okamoto, T. (2003). Technology for detecting wet bars in water-cooled stator windings of turbine generators. In Electric Machines and Drives Conference, 2003. IEMDC’03. IEEE International (pp. 1337–1343).
25.
Kim, H. S., Bae, Y. C., & Kee, C. D. (2008). Wet bar detection by using water absorption detector. Journal of Mechanical Science and Technology, 22, 1163–1173.CrossRef
26.
Park, K. M., Youn, B. D., Yoon, J. T., Hu, C., Kim, H. S., & Bae, Y. C. (2013). Health diagnostics of water-cooled power generator stator windings using a Directional Mahalanobis Distance (DMD). In 2013 IEEE Conference on Prognostics and Health Management (PHM) (pp. 1–8).
27.
Wang, Y., Miao, Q., & Pecht, M. (2011). Health monitoring of hard disk drive based on Mahalanobis distance. In Prognostics and System Health Management Conference (PHM-Shenzhen), 2011 (pp. 1–8).
28.
Youn, B. D., Park, K. M., Hu, C., Yoon, J. T., Kim, H. S., Jang, B. C., et al. (2015). Statistical health reasoning of water-cooled power generator stator bars against moisture absorption. IEEE Transactions on Energy Conversion, 30(4), 1376–1385.CrossRef
29.
Arora, P., White, R. E., & Doyle, M. (1998). Capacity fade mechanisms and side reactions in lithium-ion batteries. Journal of the Electrochemical Society, 145, 3647–3667.CrossRef
30.
Vetter, J., Novák, P., Wagner, M., Veit, C., Möller, K.-C., Besenhard, J., et al. (2005). Ageing mechanisms in lithium-ion batteries. Journal of Power Sources, 147, 269–281.CrossRef
31.
Plett, G. L. (2004). Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation. Journal of Power Sources, 134, 277–292.CrossRef
32.
Plett, G. L. (2006). Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2: Simultaneous state and parameter estimation. Journal of Power Sources, 161, 1369–1384.CrossRef
33.
Hu, C., Jain, G., Tamirisa, P., & Gorka, T. (2014). Method for estimating capacity and predicting remaining useful life of lithium-ion battery. Applied Energy, 126, 182–189.CrossRef
34.
Lee, S., Kim, J., Lee, J., & Cho, B. (2008). State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge. Journal of Power Sources, 185, 1367–1373.CrossRef
35.
Hu, C., Youn, B. D., & Chung, J. (2012). A multiscale framework with extended Kalman filter for lithium-ion battery SOC and capacity estimation. Applied Energy, 92, 694–704.CrossRef
36.
Ng, K. S., Moo, C.-S., Chen, Y.-P., & Hsieh, Y.-C. (2009). Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Applied Energy, 86, 1506–1511.CrossRef
37.
Chiang, Y.-H., Sean, W.-Y., & Ke, J.-C. (2011). Online estimation of internal resistance and open-circuit voltage of lithium-ion batteries in electric vehicles. Journal of Power Sources, 196, 3921–3932.CrossRef
38.
He, W., Williard, N., Chen, C., & Pecht, M. (2013). State of charge estimation for electric vehicle batteries using unscented Kalman filtering. Microelectronics Reliability, 53, 840–847.CrossRef
39.
Plett, G. L. (2011). Recursive approximate weighted total least squares estimation of battery cell total capacity. Journal of Power Sources, 196, 2319–2331.CrossRef
40.
Schmidt, A. P., Bitzer, M., Imre, Á. W., & Guzzella, L. (2010). Model-based distinction and quantification of capacity loss and rate capability fade in Li-ion batteries. Journal of Power Sources, 195, 7634–7638.CrossRef
41.
Verbrugge, M. (2007). Adaptive, multi-parameter battery state estimator with optimized time-weighting factors. Journal of Applied Electrochemistry, 37, 605–616.CrossRef
42.
Xiong, R., Sun, F., Chen, Z., & He, H. (2014). A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles. Applied Energy, 113, 463–476.CrossRef
43.
Waag, W., & Sauer, D. U. (2013). Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination. Applied Energy, 111, 416–427.CrossRef
44.
Bai, G., Wang, P., Hu, C., & Pecht, M. (2014). A generic model-free approach for lithium-ion battery health management. Applied Energy, 135, 247–260.CrossRef
45.
Kim, J., Lee, S., & Cho, B. (2012). Complementary cooperation algorithm based on DEKF combined with pattern recognition for SOC/capacity estimation and SOH prediction. IEEE Transactions on Power Electronics, 27, 436–451.CrossRef
46.
Eddahech, A., Briat, O., Bertrand, N., Delétage, J.-Y., & Vinassa, J.-M. (2012). Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks. International Journal of Electrical Power & Energy Systems, 42, 487–494.CrossRef
47.
Pattipati, B., Sankavaram, C., & Pattipati, K. (2011). System identification and estimation framework for pivotal automotive battery management system characteristics. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41, 869–884.CrossRef
48.
Nuhic, A., Terzimehic, T., Soczka-Guth, T., Buchholz, M., & Dietmayer, K. (2013). Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods. Journal of Power Sources, 239, 680–688.CrossRef
49.
Widodo, A., Shim, M.-C., Caesarendra, W., & Yang, B.-S. (2011). Intelligent prognostics for battery health monitoring based on sample entropy. Expert Systems with Applications, 38, 11763–11769.CrossRef
50.
Hu, C., Jain, G., Zhang, P., Schmidt, C., Gomadam, P., & Gorka, T. (2014). Data-driven method based on particle swarm optimization and k-nearest neighbor regression for estimating capacity of lithium-ion battery. Applied Energy, 129, 49–55.CrossRef
51.
Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2009). Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement, 58, 291–296.CrossRef
52.
Ng, S. S., Xing, Y., & Tsui, K. L. (2014). A naive Bayes model for robust remaining useful life prediction of lithium-ion battery. Applied Energy, 118, 114–123.CrossRef
53.
Wang, D., Miao, Q., & Pecht, M. (2013). Prognostics of lithium-ion batteries based on relevance vectors and a conditional three-parameter capacity degradation model. Journal of Power Sources, 239, 253–264.CrossRef
54.
Liu, J., Saxena, A., Goebel, K., Saha, B., & Wang, W. (2010). An adaptive recurrent neural network for remaining useful life prediction of lithium-ion batteries. DTIC Document 2010.
55.
Saha, B., & Goebel, K. (2009). Modeling Li-ion battery capacity depletion in a particle filtering framework. In Proceedings of the Annual Conference of the Prognostics and Health Management Society, 2009 (pp. 2909–2924).
56.
Kim, T., Oh, H., Kim, H., & Youn, B. D. (2016). An online-applicable model for predicting health degradation of PEM fuel cells with root cause analysis. IEEE Transactions on Industrial Electronics, 63(11), 7094–7103.CrossRef
57.
Demirci, S., Yigit, E., Eskidemir, I. H., & Ozdemir, C. (2012). Ground penetrating radar imaging of water leaks from buried pipes based on back-projection method. NDT and E International, 47, 35–42.CrossRef
58.
Gao, Y., Brennan, M., Joseph, P., Muggleton, J., & Hunaidi, O. (2004). A model of the correlation function of leak noise in buried plastic pipes. Journal of Sound and Vibration, 277, 133–148.CrossRef
59.
McNulty, J. (2001). An acoustic-based system for detecting, locating and sizing leaks in water pipelines. In Proceedings of the 4th International Conference on Water Pipeline Systems: Managing Pipeline Assets in an Evolving Market. New York, UK, 2001.
60.
Kim, T., Woo, S., Youn, B. D., & Huh, Y. C. (2015). TDR-based pipe leakage detection and location using Bayesian inference. In 2015 IEEE Conference on Prognostics and Health Management (PHM) (pp. 1–5).
61.
Schuet, S., Timucin, D., & Wheeler, K. (2011). A model-based probabilistic inversion framework for characterizing wire fault detection using TDR. IEEE Transactions on Instrumentation and Measurement, 60, 1654–1663.CrossRef
Metadaten
Titel
Case Studies: Prognostics and Health Management (PHM)
verfasst von
Chao Hu
Byeng D. Youn
Pingfeng Wang
Copyright-Jahr
2019
Buch
Engineering Design under Uncertainty and Health Prognostics

Print ISBN: 978-3-319-92572-1

Electronic ISBN: 978-3-319-92574-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-319-92574-5_9

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