Multi-sensor prognostics modeling for applications with highly incomplete signals

References

• Balzano, L., Nowak, R. and Recht, B. (2010) Online identification and tracking of subspaces from highly incomplete information, in 48th Annual Allerton Conference on Communication, Control, and Computing, IEEE Press, Piscataway, NJ, pp. 704–711.
   Google Scholar
• Balzano, L. and Wright, S. (2013) On GROUSE and incremental SVD, in 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, IEEE Press, Piscataway, NJ, pp. 1–4.
   Google Scholar
• Brand, M. (2002) Incremental singular value decomposition of uncertain data with missing values, in European Conference on Computer Vision. Springer, Berlin, Heidelberg, pp. 707–720.
   Google Scholar
• Bunch, J.R. and Nielsen, C.P. (1978) Updating the singular value decomposition. Numerische Mathematik, 31(2), 111–129.
   Web of Science ®Google Scholar
• Chehade, A., Bonk, S. and Liu, K. (2017) Sensory-based failure threshold estimation for remaining useful life prediction. IEEE Transactions on Reliability, 66(3), 939–949.
   Web of Science ®Google Scholar
• Chehade, A., Song, C., Liu, K., Saxena, A. and Zhang, X. (2018) A data-level fusion approach for degradation modeling and prognostic analysis under multiple failure modes. Journal of Quality Technology, 50(2), 150–165.
   Web of Science ®Google Scholar
• Dai, W. and Milenkovic, O. (2010) SET: An algorithm for consistent matrix completion, in International Conference on Acoustics Speech and Signal Processing, IEEE Press, Piscataway, NJ, pp. 3646–3649.
   Google Scholar
• Doray, L. (1994) IBNR reserve under a loglinear location-scale regression model, in Casualty Actuarial Society Forum, pp. 607–652.
   Google Scholar
• Fang, X., Gebraeel, N. and Paynabar, K. (2017a) Scalable prognostic models for large-scale condition monitoring applications. IISE Transactions, 49(7), 698–710.
   Web of Science ®Google Scholar
• Fang, X., Paynabar, K. and Gebraeel, N. (2017b) Multistream sensor fusion-based prognostics model for systems with single failure modes. Reliability Engineering & System Safety, 159, 322–331.
   Web of Science ®Google Scholar
• Fang, X., Zhou, R., and Gebraeel, N. (2015) An adaptive functional regression-based prognostic model for applications with missing data. Reliability Engineering & System Safety, 133, 266–274.
   Web of Science ®Google Scholar
• Gouriveau, R. and Zerhouni, N. (2012) Connexionist-systems-based long term prediction approaches for prognostics. IEEE Transactions on Reliability, 61(4), 909–920.
   Web of Science ®Google Scholar
• Jain, P., Meka, R. and Dhillon, I.S. (2010) Guaranteed rank minimization via singular value projection, in Advances in Neural Information Processing Systems, Neural Information Processing Systems, Vancouver, B.C., Canada, pp. 937–945.
   Google Scholar
• Karhunen, K. (1947) Uber lineare methoden in der wahrscheinlichkeitsrechnung. Annales Academiae Sciientiarum Fennicae, Series AI: Mathematica-Physica, 37, 3–79.
   Google Scholar
• Liao, H. and Sun, J. (2011) Nonparametric and semi-parametric sensor recovery in multichannel condition monitoring systems. IEEE Transactions on Automation Science and Engineering, 8(4), 744–753.
   Web of Science ®Google Scholar
• Liu, K., Chehade, A. and Song, C. (2015) Optimize the signal quality of the composite health index via data fusion for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 14(3), 1504–1514.
   Web of Science ®Google Scholar
• Liu, K., Gebraeel, N.Z. and Shi, J. (2013) A data-level fusion model for developing composite health indices for degradation modeling and prognostic analysis. IEEE Transactions on Automation Science and Engineering, 10(3), 652–664.
   Web of Science ®Google Scholar
• Liu, K. and Huang, S. (2014) Integration of data fusion methodology and degradation modeling process to improve prognostics. IEEE Transactions on Automation Science and Engineering, 13(1), 344–354.
   Web of Science ®Google Scholar
• Ma, S., Goldfarb, D. and Chen, L. (2011) Fixed point and Bregman iterative methods for matrix rank minimization. Mathematical Programming, 128(1), 321–353.
   Web of Science ®Google Scholar
• Mazumder, R., Hastie, T. and Tibshirani, R. (2010) Spectral regularization algorithms for learning large incomplete matrices. Journal of Machine Learning Research, 11, 2287–2322.
   PubMed Web of Science ®Google Scholar
• Moghaddass, R. and Zuo, M.J. (2014) An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process. Reliability Engineering & System Safety, 124, 92–104.
   Web of Science ®Google Scholar
• Ramsay, J.O. (2004). Functional Data Analysis.
   Google Scholar
• Saxena, A., Goebel, K., Simon, D. and Eklund, N. (2008) Damage propagation modeling for aircraft engine run-to-failure simulation. International Conference on Prognostics and Health Management, IEEE Press, Piscataway, NJ, pp.1–9.
   Google Scholar
• Song, C. and Liu, K. (2018) Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach. IISE Transactions, 50(10), 853–867.
   Web of Science ®Google Scholar
• Song, C., Liu, K. and Zhang, X. (2017) Integration of data-level fusion model and kernel methods for degradation modeling and prognostic analysis. IEEE Transactions on Reliability, 67(2), 640–650.
   Web of Science ®Google Scholar
• Song, C., Liu, K. and Zhang, X. (2019). A generic framework for multisensor degradation modeling based on supervised classification and failure surface. IISE Transactions, 51(11), 1288–1302.
   Web of Science ®Google Scholar
• Srebro, N., Rennie, J. and Jaakkola, T.S. (2005) Maximum-margin matrix factorization, in Advances in Neural Information Processing Systems pp. 1329–1336.
   Google Scholar
• Sun, J., Liao, H. and Upadhyaya, B.R. (2014) A robust functional-data-analysis method for data recovery in multichannel sensor systems. IEEE Transactions on Cybernetics, 44(8), 1420–1431.
   Web of Science ®Google Scholar
• Sun, J., Zuo, H., Yang, H. and Pecht, M. (2010) Study of ensemble learning-based fusion prognostics, in Prognostics and Health Management Conference, IEEE Press, Piscataway, NJ, pp. 1–7.
   Google Scholar
• Toh, K.C. and Yun, S. (2010). An accelerated proximal gradient algorithm for nuclear norm regularized linear least squares problems. Pacific Journal of Optimization, 6(3), 615–640.
   Web of Science ®Google Scholar
• Xu, J., Wang, Y. and Xu, L. (2014) PHM-oriented integrated fusion prognostics for aircraft engines based on sensor data. IEEE Sensors Journal, 14(4), 1124–1132.
   Web of Science ®Google Scholar
• Yan, H., Liu, K., Zhang, X. and Shi, J. (2016) Multiple sensor data fusion for degradation modeling and prognostics under multiple operational conditions. IEEE Transactions on Reliability, 65(3), 1416–1426.
   Web of Science ®Google Scholar
• Yao, F., Fu, Y. and Lee, T.C. (2011) Functional mixture regression. Biostatistics, 12(2), 341–353.
   PubMed Web of Science ®Google Scholar
• Yao, F., Müller, H.G. and Wang, J.L. (2005). Functional data analysis for sparse longitudinal data. Journal of the American Statistical Association, 100(470), 577–590.
   Web of Science ®Google Scholar
• Zhou, R., Serban, N. and Gebraeel, N. (2011) Degradation modeling applied to residual lifetime prediction using functional data analysis. The Annals of Applied Statistics, 5, 1586–1610.
   Web of Science ®Google Scholar
• Zhou, R., Gebraeel, N. and Serban, N. (2012) Degradation modeling and monitoring of truncated degradation signals. IIE Transactions, 44(9), 793–803.
   Web of Science ®Google Scholar