Method for solving the nonlinear inverse problem in gas face seal diagnosis based on surrogate models

Abstract

Physical models carry quantitative and explainable expert knowledge. However, they have not been introduced into gas face seal diagnosis tasks because of the unacceptable computational cost of inferring the input fault parameters for the observed output or solving the inverse problem of the physical model. The presented work develops a surrogate-model-assisted method for solving the nonlinear inverse problem in limited physical model evaluations. The method prepares a small initial database on sites generated with a Latin hypercube design and then performs an iterative routine that benefits from the rapidity of the surrogate models and the reliability of the physical model. The method is validated on simulated and experimental cases. Results demonstrate that the method can effectively identify the parameters that induce the abnormal signal output with limited physical model evaluations. The presented work provides a quantitative, explainable, and feasible approach for identifying the cause of gas face seal contact. It is also applicable to mechanical devices that face similar difficulties.

Graphical abstract

Keywords

surrogate model / gas face seal / fault diagnosis / nonlinear dynamics / tribology

References

[1] Fan Y E, Gu F S, Ball A. A review of the condition monitoring of mechanical seals. In: Proceedings of ASME the 7th Biennial Conference on Engineering Systems Design & Analysis. Manchester, 2004, 3: 179–184

[2] Phillips R L, Jacobs L E, Merati P. Experimental determination of the thermal characteristics of a mechanical seal and its operating environment. Tribology Transactions, 1997, 40(4): 559–568

[3] DiRussoE. Film Thickness Measurement for Spiral Groove and Rayleigh Step Lift Pad Self-Acting Face Seals. NASA TP-2058, 1982

[4] Huang W F, Lin Y B, Liu Y, Liu X F, Gao Z, Wang Y M. Face rub-impact monitoring of a dry gas seal using acoustic emission. Tribology Letters, 2013, 52(2): 253–259

[5] Towsyfyan H, Gu F S, Ball A D, Liang B. Tribological behaviour diagnostic and fault detection of mechanical seals based on acoustic emission measurements. Friction, 2019, 7(6): 572–586

[6] Daraz A, Alabied S, Zhen D, Gu F S, Ball A D. Detection and diagnosis of mechanical seal faults in centrifugal pumps based on acoustic measurement. In: Ball A, Gelman L, Rao B K N, eds. Advances in Asset Management and Condition Monitoring. Cham: Springer, 2020, 963–975

[7] Medina-Arenas M, Sopp F, Stolle J, Schley M, Kamieth R, Wassermann F. Measurement and analysis of inadequate friction mechanisms in liquid-buffered mechanical seals utilizing acoustic emission technique. Vibration, 2021, 4(1): 263–283

[8] Feldman Y, Kligerman Y, Etsion I. Stiffness and efficiency optimization of a hydrostatic laser surface textured gas seal. Journal of Tribology, 2007, 129(2): 407–410

[9] Blasiak S, Zahorulko A V. A parametric and dynamic analysis of non-contacting gas face seals with modified surfaces. Tribology International, 2016, 94: 126–137

[10] Jiang J B, Zhao W J, Peng X D, Li J Y. A novel design for discrete surface texture on gas face seals based on a superposed groove model. Tribology International, 2020, 147: 106269

[11] Zhang Z, Li X H. Acoustic emission monitoring for film thickness of mechanical seals based on feature dimension reduction and cascaded decision. In: Proceedings of 2014 the 6th International Conference on Measuring Technology and Mechatronics Automation. Zhangjiajie: IEEE, 2014, 64–70

[12] YinY, LiuX F, HuangW F, Liu Y, HuS T. Gas face seal status estimation based on acoustic emission monitoring and support vector machine regression. Advances in Mechanical Engineering, 2020, 12(5): 168781402092132

[13] ZhangZ H, Min F, ChenG S, ShenS P, WenZ C, ZhouX B. Tri-partition state alphabet-based sequential pattern for multivariate time series. Cognitive Computation, 2021 (in press)

[14] Li T Y, Qian Z J, Deng W, Zhang D Z, Lu H H, Wang S H. Forecasting crude oil prices based on variational mode decomposition and random sparse Bayesian learning. Applied Soft Computing, 2021, 113: 108032

[15] Ran X J, Zhou X B, Lei M, Tepsan W, Deng W. A novel k-means clustering algorithm with a noise algorithm for capturing urban hotspots. Applied Sciences, 2021, 11(23): 11202

[16] Miller B A, Green I. Numerical formulation for the dynamic analysis of spiral-grooved gas face seal. Journal of Tribology, 2001, 123(2): 395–403

[17] Green I. A transient dynamic analysis of mechanical seals including asperity contact and face deformation. Tribology Transactions, 2002, 45(3): 284–293

[18] Zirkelback N, San Andre’s L. Effect of frequency excitation on force coefficients of spiral groove gas seals. Journal of Tribology, 1999, 121(4): 853–861

[19] Miller B, Green I. Constitutive equations and the correspondence principle for the dynamics of gas lubricated triboelements. Journal of Tribology, 1998, 120(2): 345–352

[20] Simpson T W, Peplinski J D, Koch P H, Allen J K. Metamodels for computer-based engineering design: survey and recommendations. Engineering with Computers, 2001, 17(2): 129–150

[21] Bhosekar A, Ierapetritou M. Advances in surrogate based modeling, feasibility analysis, and optimization: a review. Computers & Chemical Engineering, 2018, 108: 250–267

[22] Han Z H. Kriging surrogate model and its application to design optimization: a review of recent progress. Acta Aeronautica et Astronautica Sinica, 2016, 37(11): 3197–3225

[23] Kontogiannis S G, Savill M A. A generalized methodology for multidisciplinary design optimization using surrogate modelling and multifidelity analysis. Optimization and Engineering, 2020, 21(3): 723–759

[24] Garg A, Liu C, Jishnu A K, Gao L, Le Phung M L, Tran V M. A Thompson sampling efficient multi-objective optimization algorithm (TSEMO) for lithium-ion battery liquid-cooled thermal management system: study of hydrodynamic, thermodynamic, and structural performance. Journal of Electrochemical Energy Conversion and Storage, 2021, 18(2): 021009

[25] Tang H S, Ren Y, Kumar A. Optimization tool based on multi-objective adaptive surrogate modeling for surface texture design of slipper bearing in axial piston pump. Alexandria Engineering Journal, 2021, 60(5): 4483–4503

[26] Liu Y, Liu Q X, Yin M, Yin G F. Dynamic analysis and structure optimization of a floating ring system in dry gas seal. Journal of Advanced Mechanical Design, Systems and Manufacturing, 2018, 12(7): JAMDSM0128

[27] Patir N, Cheng H S. An average flow model for determining effects of three-dimensional roughness on partial hydrodynamic lubrication. Journal of Tribology, 1978, 100(1): 12–17

[28] FanY B, Gu F S, BallA. Modeling acoustic emissions generated by sliding friction. Wear, 2010, 268(5–6): 811–815

[29] Greenwood J A, Williamson J B P. Contact of nominally flat surfaces. Proceedings of the Royal Society of London, 1966, 295(1442): 300–319

[30] Yin Y, Hu S T, Huang W F, Liu X F, Liu Y, Wang Z X. A bi-Gaussian acoustic emission model for sliding friction. IOP Conference Series: Material Science and Engineering, 2019, 686(1): 012026

[31] Mckay M D, Beckman R J, Conover W J. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. Technometrics, 1979, 21: 239–245

[32] Morris M D, Mitchell T J. Exploratory designs for computational experiments. Journal of Statistical Planning and Inference, 1995, 43(3): 381–402

[33] Lee B W, Peterson J J, Yin K H, Stockdale G S, Liu Y C, O’Brien A. System model development and computer experiments for continuous API manufacturing. Chemical Engineering Research & Design, 2020, 156: 495–506

[34] Sacks J, Welch W J, Mitchell T J, Wynn H P. Design and analysis of computer experiments. Statistical Science, 1989, 4(4): 409–423

[35] Yin J, Ng S H, Ng K M. Kriging metamodel with modified nugget-effect: the heteroscedastic variance case. Computers & Industrial Engineering, 2011, 61(3): 760–777

[36] Toal D J J, Bressloff N W, Keane A J. Kriging hyperparameter tuning strategies. AIAA Journal, 2008, 46(5): 1240–1252

[37] Byrd R H, Gilbert J C, Nocedal J. A trust region method based on interior point techniques for nonlinear programming. Mathematical Programming, 2000, 89(1): 149–185

[38] Herrera F, Lozano M, Verdegay J L. Tackling real-coded genetic algorithms: operators and tools for behavioural analysis. Artificial Intelligence Review, 1998, 12(4): 265–319

[39] Deng W, Zhang X X, Zhou Y Q, Liu Y, Zhou X B, Chen H L, Zhao H M. An enhanced fast non-dominated solution sorting genetic algorithm for multi-objective problems. Information Sciences, 2022, 585: 441–453

[40] Jones D R, Schonlau M, Welch W J. Efficient global optimization of expensive black-box functions. Journal of Global Optimization, 1998, 13(4): 455–492

[41] Hu S T, Huang W F, Liu X F, Wang Y M. Influence analysis of secondary O-ring seals in dynamic behavior of spiral groove gas face seals. Chinese Journal of Mechanical Engineering, 2016, 29(3): 507–514

RIGHTS & PERMISSIONS

Higher Education Press 2022