A novel sparse Bayesian learning and its application to fault diagnosis for multistation assembly systems

References

• Abellan-Nebot, J.V., Liu, J., Subirón, F.R. and Shi, J. (2012) State space modeling of variation propagation in multistation machining processes considering machining-induced variations. Journal of Manufacturing Science and Engineering, 134(2), 021002.
   Web of Science ®Google Scholar
• Bastani, K., Barazandeh, B. and Kong, Z.J. (2018) Fault diagnosis in multistation assembly systems using spatially correlated Bayesian learning algorithm. Journal of Manufacturing Science and Engineering, 140(3), 031003.
   Web of Science ®Google Scholar
• Bastani, K., Kong, Z., Huang, W., Huo, X. and Zhou, Y. (2012) Fault diagnosis using an enhanced relevance vector machine (rvm) for partially diagnosable multistation assembly processes. IEEE Transactions on Automation Science and Engineering, 10(1), 124–136.
   Web of Science ®Google Scholar
• Bastani, K., Kong, Z., Huang, W. and Zhou, Y. (2016) Compressive sensing–based optimal sensor placement and fault diagnosis for multi-station assembly processes. IIE Transactions, 48(5), 462–474.
   Web of Science ®Google Scholar
• Candès, E.J. and Wakin, M.B. (2008) An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2), 21–30.
   Web of Science ®Google Scholar
• Ceglarek, D. and Shi, J. (1996) Fixture failure diagnosis for autobody assembly using pattern recognition. Journal of Manufacturing Science and Engineering, 118(1), 55–66.
   Google Scholar
• Chen, Y., Jin, J. and Shi, J. (2004) Integration of dimensional quality and locator reliability in design and evaluation of multi-station body-in-white assembly processes. IIE transactions, 36(9), 827–839.
   Web of Science ®Google Scholar
• Cohn, I., El-Hay, T., Friedman, N. and Kupferman, R. (2010) Mean field variational approximation for continuous-time Bayesian networks. The Journal of Machine Learning Research, 11, 2745–2783.
   Web of Science ®Google Scholar
• Cotter, S.F., Rao, B.D., Engan, K. and Kreutz-Delgado, K. (2005) Sparse solutions to linear inverse problems with multiple measurement vectors. IEEE Transactions on Signal Processing, 53(7), 2477–2488.
   Web of Science ®Google Scholar
• Ding, Y., Ceglarek, D. and Shi, J. (2002) Fault diagnosis of multistage manufacturing processes by using state space approach. Journal of Manufacturing Science and Engineering, 124(2), 313–322.
   Web of Science ®Google Scholar
• Ding, Y., Ceglarek, D. and Shi, J. (2000) Modeling and diagnosis of multistage manufacturing processes: Part i state space model, in Proceedings of the 2000 Japan/USA Symposium on Flexible Automation, ASME, Ann Arbor, MI, pp. 23–26.
   Google Scholar
• Donoho, D.L. (2006) For most large underdetermined systems of linear equations the minimal l1 norm solution is also the sparsest solution. Communications on Pure and Applied Mathematics: A Journal Issued by the Courant Institute of Mathematical Sciences, 59(6), 797–829.
   Google Scholar
• Fang, J., Shen, Y., Li, F., Li, H. and Chen, Z. (2015) Support knowledge-aided sparse Bayesian learning for compressed sensing, in 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE Press, Piscataway, NJ, pp. 3786–3790.
   Google Scholar
• Guo, Y., Sun, B., Li, N. and Fang, D. (2017) Variational Bayesian inference-based counting and localization for off-grid targets with faulty prior information in wireless sensor networks. IEEE Transactions on Communications, 66(3), 1273–1283.
   Web of Science ®Google Scholar
• Han, N. and Song, Z. (2018) Bayesian multiple measurement vector problem with spatial structured sparsity patterns. Digital Signal Processing, 75, 184–201.
   Web of Science ®Google Scholar
• Han, T., Jiang, D., Sun, Y., Wang, N. and Yang, Y. (2018) Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification. Measurement, 118, 181–193.
   Web of Science ®Google Scholar
• Huang, W., Lin, J., Bezdecny, M., Kong, Z. and Ceglarek, D. (2018) Stream-of-variation modelling: Part i: A generic three-dimensional variation model for rigid-body assembly in single station assembly processes. Journal of Manufacturing Science and Engineering, 140(3), 821–831.
   Google Scholar
• Huang, W., Lin, J., Kong, Z. and Ceglarek, D. (2007) Stream-of-variation (sova) modeling; Part ii: a generic 3d variation model for rigid body assembly in multistation assembly processes. Journal of Manufacturing Science and Engineering, 129(4), 832–842.
   Web of Science ®Google Scholar
• Jin, J. and Chen, Y. (2001) Quality and reliability information integration for design evaluation of fixture system reliability. Quality and Reliability Engineering International, 17(5), 355–372.
   Web of Science ®Google Scholar
• Kong, Z., Ceglarek, D. and Huang, W. (2008) Multiple fault diagnosis method in multistation assembly processes using orthogonal diagonalization analysis. Journal of Manufacturing Science and Engineering, 130(1), 011014.
   Web of Science ®Google Scholar
• Kong, Z., Huang, W. and Oztekin, A. (2009) Variation propagation analysis for multistation assembly process with consideration of gd&t factors. Journal of Manufacturing Science and Engineering, 131(5), 051010.
   Web of Science ®Google Scholar
• Kong, Z., Kumar, R., Gogineni, S., Zhou, Y. and Ceglarek, D. (2006) Mode-based tolerance analysis in multi-station assembly using stream of variation model. Trans. NAMRI/SME, 34, 469–476.
   Google Scholar
• Lee, J., Son, J., Zhou, S. and Chen, Y. (2020) Variation source identification in manufacturing processes using bayesian approach with sparse variance components prior. IEEE Transactions on Automation Science and Engineering, 17(3), 1469–1485.
   Web of Science ®Google Scholar
• Li, H., Jiang, Y., Fang, J. and Rangaswamy, M. (2017) Adaptive subspace signal detection with uncertain partial prior knowledge. IEEE Transactions on Signal Processing, 65(16), 4394–4405.
   Web of Science ®Google Scholar
• Li, S. and Chen, Y. (2016) A Bayesian variable selection method for joint diagnosis of manufacturing process and sensor faults. IIE Transactions, 48(4), 313–323.
   Web of Science ®Google Scholar
• Liu, J. (2010) Variation reduction for multistage manufacturing processes: A comparison survey of statistical-process-control vs stream-of-variation methodologies. Quality and Reliability Engineering International, 26(7), 645–661.
   Web of Science ®Google Scholar
• Luessi, M., Babacan, S.D., Molina, R. and Katsaggelos, A.K. (2013) Bayesian simultaneous sparse approximation with smooth signals. IEEE Transactions on Signal Processing, 61(22), 5716–5729.
   Web of Science ®Google Scholar
• Petersen, K.B., Winther, O. and Hansen, L.K. (2005) On the slow convergence of EM and VBEM in low-noise linear models. Neural Computation, 17(9), 1921–1926.
   PubMed Web of Science ®Google Scholar
• Shi, J. (2023) In-process quality improvement: Concepts, methodologies, and applications. IISE Transactions, 55(1), 2–21.
   Web of Science ®Google Scholar
• Tibshirani, R. (1996) Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58(1), 267–288.
   Web of Science ®Google Scholar
• Tipping, M.E. (2001) Sparse Bayesian learning and the relevance vector machine. Journal of Machine Learning Research, 1(Jun), 211–244.
   Google Scholar
• Wipf, D. and Nagarajan, S. (2009) Solving sparse linear inverse problems: Analysis of reweighted l1 and l2 methods, in SPARS’09-Signal Processing with Adaptive Sparse Structured Representations, HAL, Saint Malo, France, pp. 1–7.
   Google Scholar
• Wipf, D.P. and Rao, B.D. (2007) An empirical Bayesian strategy for solving the simultaneous sparse approximation problem. IEEE Transactions on Signal Processing, 55(7), 3704–3716.
   Web of Science ®Google Scholar
• Yu, D., Guo, Y., Li, N. and Wang, M. (2019) Sa-m-sbl: An algorithm for CSI-based device-free localization with faulty prior information. IEEE Access, 7, 61831–61839.
   Web of Science ®Google Scholar
• Zhang, C., Yan, H., Lee, S. and Shi, J. (2021) Dynamic multivariate functional data modeling via sparse subspace learning. Technometrics, 63(3), 370–383.
   Web of Science ®Google Scholar
• Zhang, Z., Huang, W., Liao, Y., Song, Z., Shi, J., Jiang, X., Shen, C. and Zhu, Z. (2022) Bearing fault diagnosis via generalized logarithm sparse regularization. Mechanical Systems and Signal Processing, 167, 108576.
   Web of Science ®Google Scholar
• Zhang, Z. and Rao, B.D. (2011) Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning. IEEE Journal of Selected Topics in Signal Processing, 5(5), 912–926.
   Web of Science ®Google Scholar
• Zhou, S., Chen, Y. and Shi, J. (2004) Statistical estimation and testing for variation root-cause identification of multistage manufacturing processes. IEEE Transactions on Automation Science and Engineering, 1(1), 73–83.
   Web of Science ®Google Scholar