A Sparse Bayesian Learning for Diagnosis of Nonstationary and Spatially Correlated Faults:References

This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2) Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References REFERENCES [1] A. Shirazinia, S. Chatterjee, and M. Skoglund, “Analysis-by-synthesis quantization for compressed sensing measurements,” IEEE transactions on signal processing, vol. 61, no. 22, pp. 5789–5800, 2013. [2] J. Dai, A. Liu, and V. K. Lau, “Joint channel estimation and user grouping for massive mimo systems,” IEEE Transactions on Signal Processing, vol. 67, no. 3, pp. 622–637, 2018. [3] I. F. Gorodnitsky, J. S. George, and B. D. Rao, “Neuromagnetic source imaging with focuss: a recursive weighted minimum norm algorithm,” Electroencephalography and clinical Neurophysiology, vol. 95, no. 4, pp. 231–251, 1995. [4] J. H. Ender, “On compressive sensing applied to radar,” Signal Processing, vol. 90, no. 5, pp. 1402–1414, 2010. [5] K. Bastani, Z. Kong, W. Huang, X. Huo, and Y. Zhou, “Fault diagnosis using an enhanced relevance vector machine (rvm) for partially diagnosable multistation assembly processes,” IEEE Transactions on Automation Science and Engineering, vol. 10, no. 1, pp. 124–136, 2012. [6] X. Li, Y. Yang, I. Bennett, and D. Mba, “Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis,” Mechanical Systems and Signal Processing, vol. 131, pp. 348–363, 2019. [7] K. Bastani, Z. Kong, W. Huang, and Y. Zhou, “Compressive sensing– based optimal sensor placement and fault diagnosis for multi-station assembly processes,” IIE Transactions, vol. 48, no. 5, pp. 462–474, 2016. [8] K. Bastani, B. Barazandeh, and Z. J. Kong, “Fault diagnosis in multistation assembly systems using spatially correlated bayesian learning algorithm,” Journal of Manufacturing Science and Engineering, vol. 140, no. 3, 2018. [9] J. Chung, B. Shen, and Z. Kong, “A novel sparse bayesian learning and its application to fault diagnosis for multistation assembly systems,” IISE Transactions, pp. 1–14, 2023. [10] J. Lee, J. Son, S. Zhou, and Y. Chen, “Variation source identification in manufacturing processes using bayesian approach with sparse variance components prior,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 3, pp. 1469–1485, 2020. [11] W. Huang, J. Lin, Z. Kong, and D. Ceglarek, “Stream-of-variation (sova) modeling ii: a generic 3d variation model for rigid body assembly in multistation assembly processes,” Journal of Manufacturing Science and Engineering, vol. 129, no. 4, 2007. [12] W. Huang, J. Lin, M. Bezdecny, Z. Kong, and D. Ceglarek, “Stream-ofvariation modeling—part i: A generic three-dimensional variation model for rigid-body assembly in single station assembly processes,” Journal of Manufacturing Science and Engineering, vol. 140, no. 3, 2018. [13] Y. Ding, D. Ceglarek, and J. Shi, “Fault diagnosis of multistage manufacturing processes by using state space approach,” J. Manuf. Sci. Eng., vol. 124, no. 2, pp. 313–322, 2002. [14] Z. Kong, D. Ceglarek, and W. Huang, “Multiple fault diagnosis method in multistation assembly processes using orthogonal diagonalization analysis,” Journal of manufacturing science and engineering, vol. 130, no. 1, 2008. [15] E. J. Candes and Y. Plan, “Near-ideal model selection by ` ℓ1 minimization,” The Annals of Statistics, vol. 37, no. 5A, pp. 2145–2177, 2009. [16] Z. Zhang and B. D. Rao, “Sparse signal recovery with temporally correlated source vectors using sparse bayesian learning,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 5, pp. 912–926, 2011. [17] R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 58, no. 1, pp. 267–288, 1996. [18] E. J. Candes and M. B. Wakin, “An introduction to compressive ` sampling,” IEEE signal processing magazine, vol. 25, no. 2, pp. 21– 30, 2008. [19] S. Li and Y. Chen, “A bayesian variable selection method for joint diagnosis of manufacturing process and sensor faults,” IIE transactions, vol. 48, no. 4, pp. 313–323, 2016. [20] S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz-Delgado, “Sparse solutions to linear inverse problems with multiple measurement vectors,” IEEE Transactions on Signal Processing, vol. 53, no. 7, pp. 2477–2488, 2005. [21] Z. Kong, R. Kumar, S. Gogineni, Y. Zhou, and D. Ceglarek, “Modebased tolerance analysis in multi-station assembly using stream of variation model,” Trans. NAMRI/SME, vol. 34, pp. 469–476, 2006. [22] J. V. Abellan-Nebot, J. Liu, F. R. Subiron, and J. Shi, “State space ´ modeling of variation propagation in multistation machining processes considering machining-induced variations,” Journal of Manufacturing Science and Engineering, vol. 134, no. 2, 2012. [23] J. Shi, “In-process quality improvement: Concepts, methodologies, and applications,” IISE Transactions, no. just-accepted, pp. 1–34, 2022. [24] K. B. Petersen, O. Winther, and L. K. Hansen, “On the slow convergence of em and vbem in low-noise linear models,” Neural computation, vol. 17, no. 9, pp. 1921–1926, 2005. [25] H. Wang, Q. Huang, and R. Katz, “Multi-operational machining processes modeling for sequential root cause identification and measurement reduction,” 2005. [26] H. Wang and Q. Huang, “Error cancellation modeling and its application to machining process control,” IIE transactions, vol. 38, no. 4, pp. 355– 364, 2006. [27] T. Han, D. Jiang, Y. Sun, N. Wang, and Y. Yang, “Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification,” Measurement, vol. 118, pp. 181–193, 2018. [28] A. Wang and J. Shi, “Holistic modeling and analysis of multistage manufacturing processes with sparse effective inputs and mixed profile outputs,” IISE Transactions, vol. 53, no. 5, pp. 582–596, 2021. [29] J. Dai and H. C. So, “Group-sparsity learning approach for bearing fault diagnosis,” IEEE Transactions on Industrial Informatics, vol. 18, no. 7, pp. 4566–4576, 2021. [30] M. E. Tipping, “Sparse bayesian learning and the relevance vector machine,” Journal of machine learning research, vol. 1, no. Jun, pp. 211–244, 2001. [31] D. Wipf and S. Nagarajan, “Solving sparse linear inverse problems: Analysis of reweighted ℓ1 and ℓ2 methods,” in SPARS’09-Signal Processing with Adaptive Sparse Structured Representations, 2009. [32] D. P. Wipf and B. D. Rao, “An empirical bayesian strategy for solving the simultaneous sparse approximation problem,” IEEE Transactions on Signal Processing, vol. 55, no. 7, pp. 3704–3716, 2007. [33] S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM review, vol. 43, no. 1, pp. 129–159, 2001. [34] E. J. Candes, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted ℓ1 1 minimization,” Journal of Fourier analysis and applications, vol. 14, no. 5, pp. 877–905, 2008. [35] Z. Zhang and B. D. Rao, “Extension of sbl algorithms for the recovery of block sparse signals with intra-block correlation,” IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 2009–2015, 2013. [36] J. Fang, Y. Shen, H. Li, and P. Wang, “Pattern-coupled sparse bayesian learning for recovery of block-sparse signals,” IEEE Transactions on Signal Processing, vol. 63, no. 2, pp. 360–372, 2014. [37] N. Han and Z. Song, “Bayesian multiple measurement vector problem with spatial structured sparsity patterns,” Digital Signal Processing, vol. 75, pp. 184–201, 2018. [38] L. Wang, L. Zhao, G. Bi, C. Wan, L. Zhang, and H. Zhang, “Novel wideband doa estimation based on sparse bayesian learning with dirichlet process priors,” IEEE Transactions on Signal Processing, vol. 64, no. 2, pp. 275–289, 2015. [39] D. P. Wipf and B. D. Rao, “Sparse bayesian learning for basis selection,” IEEE Transactions on Signal processing, vol. 52, no. 8, pp. 2153–2164, 2004. [40] S. Ji, Y. Xue, and L. Carin, “Bayesian compressive sensing,” IEEE Transactions on signal processing, vol. 56, no. 6, pp. 2346–2356, 2008. [41] Y. Ban, X. Alameda-Pineda, L. Girin, and R. Horaud, “Variational bayesian inference for audio-visual tracking of multiple speakers,” IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 5, pp. 1761–1776, 2019. [42] C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning. Springer, 2006. [43] I. Cohn, T. El-Hay, N. Friedman, and R. Kupferman, “Mean field variational approximation for continuous-time bayesian networks,” The Journal of Machine Learning Research, vol. 11, pp. 2745–2783, 2010. [44] L. N. Trefethen and D. Bau III, Numerical linear algebra. Siam, 1997, vol. 50. [45] D. L. Donoho, “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, vol. 59, no. 6, pp. 797–829, 2006. [46] J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (roc) curve.” Radiology, vol. 143, no. 1, pp. 29–36, 1982. [47] Z. Kong, W. Huang, and A. Oztekin, “Variation propagation analysis for multistation assembly process with consideration of gd&t factors,” Journal of Manufacturing Science and Engineering, vol. 131, no. 5, 2009. received his B.S. degree in Industrial Engineering from the Hanyang University, Seoul, Korea, in 2015. He obtained his M.S. degree in Industrial and Systems Engineering at Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 2017. He received a Ph.D. degree in Industrial and Systems Engineering from Virginia Tech, Blacksburg, VA, USA. His research interests include statistical learning and data analytics in smart manufacturing. (Member, IEEE) received the B.S. and M.S. degrees in mechanical engineering from Harbin Institute of Technology, Harbin, China, in 1993 and 1995, respectively, and the Ph.D. degree from the Department of Industrial and System Engineering, University of Wisconsin–Madison, Madison, WI, USA, in 2004. He is currently a Professor with the Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA. His research interests include sensing and analytics for smart manufacturing, and modeling, synthesis, and diagnosis for large and complex manufacturing systems. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2) Zhenyu (James) Kong. This paper is available on arxiv under CC 4.0 license. Authors: (1) Jihoon Chung; (2) Zhenyu (James) Kong. Table of Links Abstract & Introduction Review of Related Work Proposed Research Methodology Numerical Case Studies Real-World Simulation Case Studies Conclusion References Abstract & Introduction Abstract & Introduction Review of Related Work Review of Related Work Proposed Research Methodology Proposed Research Methodology Numerical Case Studies Numerical Case Studies Real-World Simulation Case Studies Real-World Simulation Case Studies Conclusion Conclusion References References REFERENCES [1] A. Shirazinia, S. Chatterjee, and M. Skoglund, “Analysis-by-synthesis quantization for compressed sensing measurements,” IEEE transactions on signal processing, vol. 61, no. 22, pp. 5789–5800, 2013. [2] J. Dai, A. Liu, and V. K. Lau, “Joint channel estimation and user grouping for massive mimo systems,” IEEE Transactions on Signal Processing, vol. 67, no. 3, pp. 622–637, 2018. [3] I. F. Gorodnitsky, J. S. George, and B. D. Rao, “Neuromagnetic source imaging with focuss: a recursive weighted minimum norm algorithm,” Electroencephalography and clinical Neurophysiology, vol. 95, no. 4, pp. 231–251, 1995. [4] J. H. Ender, “On compressive sensing applied to radar,” Signal Processing, vol. 90, no. 5, pp. 1402–1414, 2010. [5] K. Bastani, Z. Kong, W. Huang, X. Huo, and Y. Zhou, “Fault diagnosis using an enhanced relevance vector machine (rvm) for partially diagnosable multistation assembly processes,” IEEE Transactions on Automation Science and Engineering, vol. 10, no. 1, pp. 124–136, 2012. [6] X. Li, Y. Yang, I. Bennett, and D. Mba, “Condition monitoring of rotating machines under time-varying conditions based on adaptive canonical variate analysis,” Mechanical Systems and Signal Processing, vol. 131, pp. 348–363, 2019. [7] K. Bastani, Z. Kong, W. Huang, and Y. Zhou, “Compressive sensing– based optimal sensor placement and fault diagnosis for multi-station assembly processes,” IIE Transactions, vol. 48, no. 5, pp. 462–474, 2016. [8] K. Bastani, B. Barazandeh, and Z. J. Kong, “Fault diagnosis in multistation assembly systems using spatially correlated bayesian learning algorithm,” Journal of Manufacturing Science and Engineering, vol. 140, no. 3, 2018. [9] J. Chung, B. Shen, and Z. Kong, “A novel sparse bayesian learning and its application to fault diagnosis for multistation assembly systems,” IISE Transactions, pp. 1–14, 2023. [10] J. Lee, J. Son, S. Zhou, and Y. Chen, “Variation source identification in manufacturing processes using bayesian approach with sparse variance components prior,” IEEE Transactions on Automation Science and Engineering, vol. 17, no. 3, pp. 1469–1485, 2020. [11] W. Huang, J. Lin, Z. Kong, and D. Ceglarek, “Stream-of-variation (sova) modeling ii: a generic 3d variation model for rigid body assembly in multistation assembly processes,” Journal of Manufacturing Science and Engineering, vol. 129, no. 4, 2007. [12] W. Huang, J. Lin, M. Bezdecny, Z. Kong, and D. Ceglarek, “Stream-ofvariation modeling—part i: A generic three-dimensional variation model for rigid-body assembly in single station assembly processes,” Journal of Manufacturing Science and Engineering, vol. 140, no. 3, 2018. [13] Y. Ding, D. Ceglarek, and J. Shi, “Fault diagnosis of multistage manufacturing processes by using state space approach,” J. Manuf. Sci. Eng., vol. 124, no. 2, pp. 313–322, 2002. [14] Z. Kong, D. Ceglarek, and W. Huang, “Multiple fault diagnosis method in multistation assembly processes using orthogonal diagonalization analysis,” Journal of manufacturing science and engineering, vol. 130, no. 1, 2008. [15] E. J. Candes and Y. Plan, “Near-ideal model selection by ` ℓ1 minimization,” The Annals of Statistics, vol. 37, no. 5A, pp. 2145–2177, 2009. [16] Z. Zhang and B. D. Rao, “Sparse signal recovery with temporally correlated source vectors using sparse bayesian learning,” IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 5, pp. 912–926, 2011. [17] R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society: Series B (Methodological), vol. 58, no. 1, pp. 267–288, 1996. [18] E. J. Candes and M. B. Wakin, “An introduction to compressive ` sampling,” IEEE signal processing magazine, vol. 25, no. 2, pp. 21– 30, 2008. [19] S. Li and Y. Chen, “A bayesian variable selection method for joint diagnosis of manufacturing process and sensor faults,” IIE transactions, vol. 48, no. 4, pp. 313–323, 2016. [20] S. F. Cotter, B. D. Rao, K. Engan, and K. Kreutz-Delgado, “Sparse solutions to linear inverse problems with multiple measurement vectors,” IEEE Transactions on Signal Processing, vol. 53, no. 7, pp. 2477–2488, 2005. [21] Z. Kong, R. Kumar, S. Gogineni, Y. Zhou, and D. Ceglarek, “Modebased tolerance analysis in multi-station assembly using stream of variation model,” Trans. NAMRI/SME, vol. 34, pp. 469–476, 2006. [22] J. V. Abellan-Nebot, J. Liu, F. R. Subiron, and J. Shi, “State space ´ modeling of variation propagation in multistation machining processes considering machining-induced variations,” Journal of Manufacturing Science and Engineering, vol. 134, no. 2, 2012. [23] J. Shi, “In-process quality improvement: Concepts, methodologies, and applications,” IISE Transactions, no. just-accepted, pp. 1–34, 2022. [24] K. B. Petersen, O. Winther, and L. K. Hansen, “On the slow convergence of em and vbem in low-noise linear models,” Neural computation, vol. 17, no. 9, pp. 1921–1926, 2005. [25] H. Wang, Q. Huang, and R. Katz, “Multi-operational machining processes modeling for sequential root cause identification and measurement reduction,” 2005. [26] H. Wang and Q. Huang, “Error cancellation modeling and its application to machining process control,” IIE transactions, vol. 38, no. 4, pp. 355– 364, 2006. [27] T. Han, D. Jiang, Y. Sun, N. Wang, and Y. Yang, “Intelligent fault diagnosis method for rotating machinery via dictionary learning and sparse representation-based classification,” Measurement, vol. 118, pp. 181–193, 2018. [28] A. Wang and J. Shi, “Holistic modeling and analysis of multistage manufacturing processes with sparse effective inputs and mixed profile outputs,” IISE Transactions, vol. 53, no. 5, pp. 582–596, 2021. [29] J. Dai and H. C. So, “Group-sparsity learning approach for bearing fault diagnosis,” IEEE Transactions on Industrial Informatics, vol. 18, no. 7, pp. 4566–4576, 2021. [30] M. E. Tipping, “Sparse bayesian learning and the relevance vector machine,” Journal of machine learning research, vol. 1, no. Jun, pp. 211–244, 2001. [31] D. Wipf and S. Nagarajan, “Solving sparse linear inverse problems: Analysis of reweighted ℓ1 and ℓ2 methods,” in SPARS’09-Signal Processing with Adaptive Sparse Structured Representations, 2009. [32] D. P. Wipf and B. D. Rao, “An empirical bayesian strategy for solving the simultaneous sparse approximation problem,” IEEE Transactions on Signal Processing, vol. 55, no. 7, pp. 3704–3716, 2007. [33] S. S. Chen, D. L. Donoho, and M. A. Saunders, “Atomic decomposition by basis pursuit,” SIAM review, vol. 43, no. 1, pp. 129–159, 2001. [34] E. J. Candes, M. B. Wakin, and S. P. Boyd, “Enhancing sparsity by reweighted ℓ1 1 minimization,” Journal of Fourier analysis and applications, vol. 14, no. 5, pp. 877–905, 2008. [35] Z. Zhang and B. D. Rao, “Extension of sbl algorithms for the recovery of block sparse signals with intra-block correlation,” IEEE Transactions on Signal Processing, vol. 61, no. 8, pp. 2009–2015, 2013. [36] J. Fang, Y. Shen, H. Li, and P. Wang, “Pattern-coupled sparse bayesian learning for recovery of block-sparse signals,” IEEE Transactions on Signal Processing, vol. 63, no. 2, pp. 360–372, 2014. [37] N. Han and Z. Song, “Bayesian multiple measurement vector problem with spatial structured sparsity patterns,” Digital Signal Processing, vol. 75, pp. 184–201, 2018. [38] L. Wang, L. Zhao, G. Bi, C. Wan, L. Zhang, and H. Zhang, “Novel wideband doa estimation based on sparse bayesian learning with dirichlet process priors,” IEEE Transactions on Signal Processing, vol. 64, no. 2, pp. 275–289, 2015. [39] D. P. Wipf and B. D. Rao, “Sparse bayesian learning for basis selection,” IEEE Transactions on Signal processing, vol. 52, no. 8, pp. 2153–2164, 2004. [40] S. Ji, Y. Xue, and L. Carin, “Bayesian compressive sensing,” IEEE Transactions on signal processing, vol. 56, no. 6, pp. 2346–2356, 2008. [41] Y. Ban, X. Alameda-Pineda, L. Girin, and R. Horaud, “Variational bayesian inference for audio-visual tracking of multiple speakers,” IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 5, pp. 1761–1776, 2019. [42] C. M. Bishop and N. M. Nasrabadi, Pattern recognition and machine learning. Springer, 2006. [43] I. Cohn, T. El-Hay, N. Friedman, and R. Kupferman, “Mean field variational approximation for continuous-time bayesian networks,” The Journal of Machine Learning Research, vol. 11, pp. 2745–2783, 2010. [44] L. N. Trefethen and D. Bau III, Numerical linear algebra. Siam, 1997, vol. 50. [45] D. L. Donoho, “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, vol. 59, no. 6, pp. 797–829, 2006. [46] J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (roc) curve.” Radiology, vol. 143, no. 1, pp. 29–36, 1982. [47] Z. Kong, W. Huang, and A. Oztekin, “Variation propagation analysis for multistation assembly process with consideration of gd&t factors,” Journal of Manufacturing Science and Engineering, vol. 131, no. 5, 2009. received his B.S. degree in Industrial Engineering from the Hanyang University, Seoul, Korea, in 2015. He obtained his M.S. degree in Industrial and Systems Engineering at Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Korea, in 2017. He received a Ph.D. degree in Industrial and Systems Engineering from Virginia Tech, Blacksburg, VA, USA. His research interests include statistical learning and data analytics in smart manufacturing. (Member, IEEE) received the B.S. and M.S. degrees in mechanical engineering from Harbin Institute of Technology, Harbin, China, in 1993 and 1995, respectively, and the Ph.D. degree from the Department of Industrial and System Engineering, University of Wisconsin–Madison, Madison, WI, USA, in 2004. He is currently a Professor with the Grado Department of Industrial and Systems Engineering, Virginia Tech, Blacksburg, VA, USA. His research interests include sensing and analytics for smart manufacturing, and modeling, synthesis, and diagnosis for large and complex manufacturing systems.