Jianbin Guo

The method of system reliability modeling based on Hybrid theory

Traditional system reliability model is more concerned about discrete failure events, without considering the impact on reliability by systems continuous dynamic process. This paper presents a method of system reliability modeling based on Hybrid theory, which combines the continuous dynamic process and the discrete failure events together during the system, and make it easier to describe the impact on system reliability of the interaction of continuous and discrete behavior of the system. Firstly, Hybrid Automata model is extended, and the event correlation constraint is introduced to describe the conjunction relationship of failure in the modeling process. Secondly, based on an improved Hybrid automaton model, several kinds of modeling approach of the discrete failure and the continuous state interaction is given. In the simulation process, either the automatic switch of the failure state can be achieved by the real-time decision of the continuous state of the system; the mutations of continuous state caused by the discrete failure can also be described. The propagation of the discrete failure is considered at the same time. Finally, a hydraulic case is modeled by this method using MATLAB/State flow, and the results illustrate the validity of the method.

A mechanism reliability analysis method based on polynomial chaos expansion

Mechanism reliability analysis method is proposed based on polynomial chaos expansion, which is adopted to approximate to the limit function in this paper. The computational efficiency of reliability analysis is improved while achieving the same accuracy level. Firstly, uncertainty input variables are transformed into standard Gaussian distribution variables and the limit function is expressed as polynomial chaos expansion using Hermite polynomial bases. Thus the complexity of polynomial chaos expansion is greatly simplified with orthogonal characteristics between Hermite polynomial and standard Gaussian distribution probability density function. Then, the Probability Collocation method is adopted to determine the coefficient of polynomial chaos expansion and achieve complete polynomial chaos expansion. On this basis, the random simulation method is used to compute the mechanism reliability. Finally, reliability analysis is carried out with this method to a four-link mechanism. After the contrast with Monte Carlo method, the results indicate that polynomial chaos expansion improves the computational efficiency while achieving the same accuracy level.

Decomposition method for hierarchical Multidisciplinary Robust Design considering uncertainty functions and coupling variables

Exiting decomposition methods based on Function Dependency Table (FDT), those ignore the substantive impact to optimization process brought by performing uncertainty analysis, and cannot decomposes coupling variables, are not suitable for complex Multidisciplinary Robust Design (MRD).To obtain optimal decomposition form for a MRD problem, a novel decomposition method, which can deal with uncertainty functions and coupling variables in MRD, is developed. Compared with existing decomposition methods, this method is more suitable for MRD with two advantages. First, uncertainty functions can be decomposed averagely to obtain better concurrency and less total computational cost. Second, coupling variables can be identified and decomposed appropriately. This method is verified by a gear reducer box problem. Compared with two existing decomposition solutions, this method significantly reduces the total computational cost.