life prediction for critical components ... improvements in reliability assessment and life predic- ... investigates a design for the fail-safe mechanism of a.
Editorial
Physics-of-failure-based reliability and life prediction for critical components
Engineering critical components, such as turbine disks, blades, and reactor pressure vessels, are widely used in major equipment. The structural integrity of these critical components is the primary focus of ensuring safety of major equipment, which requires to be assured throughout the entire operating life. Due to unexpected aging effects, mechanical properties of critical components often require safety consideration related to the mechanisms involved in aging, including multi-physics failure mechanisms, such as fatigue, creep, corrosion, and thermal aging.1,2 As the next generation of major equipment, such as aircraft engine, steam turbine, and nuclear reactors, should work under extreme harsh conditions, continued improvements in reliability assessment and life prediction of critical components have been possible through the accurate modeling of multi-physics failure mechanisms and the introduction of advanced processing approaches.3–5 Based on physics-of-failure (PoF) modeling, their performance degradation assessment, system reliability modeling, and life estimation should be conducted to maximize lifetime and optimize inspection and maintenance policy of critical components. Moreover, failure occurs under influence of diverse uncertainties, including load variations in usage, material properties, geometry variations within tolerances, and other uncontrolled variations.6,7 Thus, probabilistic methods and tools are required to account for these uncertainties. In recent years, a large number of advanced analytical, computational, and experimental techniques have been developed. Some examples can be found in the study of Jiang et al.8 and Cang et al.,9 which greatly contribute to the exploration of PoF-based methods for reliability and life prediction of critical components. The purpose of this special collection is to provide an opportunity for researchers working in academy or industry to show their latest theoretical, numerical, and experimental aspects of structural life and reliability assessment and field applications of critical components; more recent progress can be found in the study of Yan et al.,1 Zhu et al.,4 Tahir et al.,5 Jiang et al.,8 Wang et al.,10 and Huang et al.11 This special collection contains 23 contributions of authors coming from seven
Advances in Mechanical Engineering 2017, Vol. 9(10) 1–3 Ó The Author(s) 2017 DOI: 10.1177/1687814017734930 journals.sagepub.com/home/ade
countries. These papers were ranging on the fields of interest from theoretical point of views to more practical applications. We hope that such a frontier of PoFbased reliability and life prediction could be continued to track the updated trend year by year. An introductory review of the accepted papers is presented here. In the topic of PoF modeling and analysis, an invited review article entitled ‘‘A critical review of constitutive models for solders in electronic packaging’’ by G Chen et al. reviews the constitutive models developed for simulating the complex stress and strain response of solders, in which the constitutive models with and without the definition of yield surface were discussed, including their prediction capabilities, application scopes, merits and shortcomings, and objective suggestions for further development of constitutive models for solders in electronic reliability design. ‘‘Study on the high temperature corrosion behavior of superheater steels of biomass-fired boiler in molten alkali salts mixtures’’ by J He et al. investigates hot corrosion behaviors of materials in molten alkali salts’ mixtures. ‘‘Local stress analysis of a defective rolling bearing using an explicit dynamic method’’ by Z Zhang et al. analyzes the localized stress in a defect zone of the rolling bearings using the explicit dynamic finite element (FE) simulation when the rolling elements pass through the defect. ‘‘Fatigue life analysis based on six sigma robust optimization for pantograph CHS’’ by Y Li et al. conducts fatigue reliability analysis of a pantograph collector head support based on six sigma robust optimization, which considers the random effects of material properties, external loads, and dimensions on its fatigue life. ‘‘Fail-safe design and analysis for the guide vane of a hydro turbine’’ by BA Budiman et al. investigates a design for the fail-safe mechanism of a guide vane in a Francis-type hydro turbine. ‘‘Effects of squeeze force on static behavior of riveted lap joints’’ by H. Huan et al. studies the effects of the squeeze force used in the rivet installing process on structural behavior of the riveted lap joints by FE simulation and riveted lap joints experiments. ‘‘A new dynamic sevenstage model for thickness prediction of the film between valve plate and cylinder block in axial piston pumps’’
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2 by C Zhang et al. presents a dynamic seven-stage model for thickness prediction of the film between valve plate and cylinder block in axial piston pumps. In the topic of structural health monitoring (SHM), ‘‘A prognostics approach based on the evolution of damage precursors using dynamic Bayesian networks’’ by E Rabiei et al. presents a SHM and damage prognostics framework based on evolution of damage precursors representing the indirect damage indicators, which allows the integration of any related sources of information in order to reduce the inherent uncertainties. ‘‘Dynamic extreme stress prediction of bridges based on nonlinear MGPF algorithm and SHM data’’ by F Xue et al. develops an approach combining nonlinear mixed Gaussian particle filtering algorithm with SHM data to predict the structural stress under uncertainty in real time. ‘‘A Bayesian LS-SVM method for predicting the remaining useful life of a microwave component’’ by F Sun et al. develops a Bayesian leastsquares support vector machine method by combining least-squares support vector machine with Bayesian inference for predicting the remaining useful life of a microwave component. ‘‘Failure prognosis of multiple uncertainty system based on Kalman filter and its application to aircraft fuel system’’ by J Chen et al. presents an advanced failure prognosis method with Kalman filter to address multiple uncertainty sources for an aircraft fuel feeding system health monitoring. In the topic of reliability modeling and analysis, ‘‘Expected impact quantification based reliability assessment methodology for Chilean copper smelting process—A case study’’ by F Kristjanpoller et al. quantifies the expected impact of the Chilean copper smelting process using reliability, availability, and maintainability indicators. ‘‘Reliability modeling of multiple performance based on degradation values distribution’’ by H Li et al. presents a framework for reliability assessment of products by considering the multiple performance degradation. ‘‘Reliability models of belt drive systems under slipping failure mode’’ by P Gao et al. develops a dynamic reliability model and failure rate model of belt drive systems under slipping. ‘‘Reliability sensitivity numerical analysis of mechanical structure based on Gamma processes’’ by Y Zhang et al. conducts reliability sensitivity numerical analysis of mechanical structure based on Gamma processes to describe uncertain parameters in structural systems. ‘‘Node importance measure in linear wireless sensor networks’’ by N Wang et al. analyzes the node importance in linear wireless sensor networks and identifies key states of nodes that affect the wireless sensor network performance. ‘‘Hybrid reliability analysis of structural fatigue life based on Taylor expansion method’’ by G Meng et al. calculated structural failure probability under fatigue by dealing with uncertain problems with both random and interval variables using a Taylor expansion. ‘‘Structural reliability
Advances in Mechanical Engineering analysis of multiple limit state functions using multiinput multi-output support vector machine’’ by HS Li et al. conducts structural reliability analysis with multiple limit state functions using support vector machine technique and develops a sole support vector machine surrogate model for all limit state functions. In the topic of design optimization under uncertainty, ‘‘Reliability-based multidisciplinary design and optimization for twin-web disk using adaptive Kriging Surrogate model’’ by M Zhang et al. performs reliability-based multidisciplinary design optimization (MDO) to find a proper shape of twin-web disk with the minimum weight. ‘‘Multidisciplinary reliability design optimization under time-varying uncertainties’’ by H Xu et al. puts forward a multidisciplinary reliability design optimization method to handle time-varying uncertainties in mechanical systems. ‘‘Multi-objective reliability design optimization of EMU pantograph geometric parameters’’ by B Chen et al. conducts multi-objective reliability design optimization to reduce the design variable fluctuations in the multi-objective optimization of the pantograph geometric parameters of electric multiple units. ‘‘A concurrent reliability optimization procedure in the earlier design phases of complex engineering systems under epistemic uncertainties’’ by D Meng et al. develops an evidence-based collaborative reliability optimization method to quantify the effects of epistemic uncertainties in an aircraft conceptual design. ‘‘A multidisciplinary coupling relationship coordination algorithm using the hierarchical control methods of complex systems and its application in MDO’’ by R Yuan et al. introduces a hierarchical control method–based coupling relationship coordination algorithm to solve MDO problems. As aforementioned, the topics addressed in this special collection cover a broad range of research issues that directly influence the research and development activities in the field of reliability-based design and life prediction; however, the selected topics and papers are not a comprehensive representation of the area of this special collection. We sincerely hope that the readers of the Advances in Mechanical Engineering will enjoy reading this collection of scientific contributions not only the published papers on reliability-based design and life prediction but also other important issues and possible solutions reported in relevant papers. Acknowledgements The authors are extremely grateful to all the authors for their contributions to this special issue and all the reviewers for their time and valuable comments and suggestions. The management of this special collection has been supported by the National Natural Science Foundation of China (11672070 and 51371082). They would like to express appreciation to Katrina Newitt (Senior Peer Review Manager, SAGE Publishing) for her time and efforts in the publication of this
Editorial special collection and the support of the publisher and the editorial board of the journal for organizing this collection.
Declaration of conflicting interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding The author(s) received no financial support for the research, authorship, and/or publication of this article.
Shun-Peng Zhu1, Xiancheng Zhang2, Chao Jiang3, Yongming Liu4 and Zhiyong Huang5 1 Center for System Reliability & Safety, University of Electronic Science and Technology of China, Chengdu, China 2 Key Laboratory of Pressure Systems and Safety, Ministry of Education, East China University of Science and Technology, Shanghai, China 3 State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China 4 School for Engineering of Matter, Transport & Energy, Arizona State University, Tempe, AZ, USA 5 School of Aeronautics and Astronautics, Sichuan University, Chengdu, China References 1. Yan XL, Zhang XC, Tu ST, et al. Review of creep– fatigue endurance and life prediction of 316 stainless steels. Int J Pres Ves Pip 2015; 126–127: 17–28.
3 2. Huang ZY, Wagner D, Wang QY, et al. A low cycle fatigue model for low carbon manganese steel including the effect of dynamic strain aging. Mater Sci Eng: A 2016; 654: 77–84. 3. Jiang C, Zhang W, Han X, et al. A vine-copula-based reliability analysis method for structures with multidimensional correlation. J Mech Design 2015; 137: 061405. 4. Zhu SP, Foletti S and Beretta S. Probabilistic framework for multiaxial LCF assessment under material variability. Int J Fatigue 2017; 103: 371–385. 5. Tahir F, Dahire S and Liu Y. Image-based creep-fatigue damage mechanism investigation of Alloy 617 at 950°C. Mater Sci Eng: A 2017; 679: 391–400. 6. Zhu SP, Huang HZ, Peng W, et al. Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty. Reliab Eng Syst Safe 2016; 146: 1–12. 7. Zhu SP, Huang HZ, Smith R, et al. Bayesian framework for probabilistic low cycle fatigue life prediction and uncertainty modeling of aircraft turbine disk alloys. Probabilist Eng Mech 2013; 34: 114–122. 8. Jiang C, Fang T, Wang ZX, et al. A general solution framework for time-variant reliability based design optimization. Comput Method Appl M 2017; 323: 330–352. 9. Cang R, Xu Y, Chen S, et al. Microstructure representation and reconstruction of heterogeneous materials via deep belief network for computational material design. J Mech Design 2017; 139: 071404. 10. Wang RZ, Zhang XC, Gong JG, et al. Creep-fatigue life prediction and interaction diagram in nickel-based GH4169 superalloy at 650°C based on cycle-by-cycle concept. Int J Fatigue 2017; 97: 114–123. 11. Huang ZY, Liu HQ, Wang HM, et al. Effect of stress ratio on VHCF behavior for a compressor blade titanium alloy. Int J Fatigue 2016; 93: 232–237.
