Dynamic Reliability Sensitivity Analysis of Mechanical Components

In this paper, the variation rules of strength, load, reliability and failure rate of mechanical components are studied with time, and a state equation model for dynamic reliability of mechanical components is established under random load. Meanwhile, reliability index is obtained by using Second moment method and perturbation method. Based on the reliability design theory and sensitivity analysis method, torsion-bar is taken as an example, and the dynamic reliability sensitivity of torsion-bar is extensively discussed and a computing method is presented for dynamic reliability sensitivity design. The variation rules of dynamic reliability sensitivity are obtained and the effects of design parameters on reliability of torsion-bar are studied. As a result, the proposed method provides theoretical basis for reliability design of torsion bar.

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Fatigue Reliability Sensitivity Analysis of Complex Mechanical Components under Random Excitation

Mathematical Problems in Engineering ◽

10.1155/2011/586316 ◽

2011 ◽

Vol 2011 ◽

pp. 1-17 ◽

Cited By ~ 1

Author(s):

Hao Lu ◽

Yimin Zhang ◽

Xufang Zhang ◽

Xianzhen Huang

Keyword(s):

Sensitivity Analysis ◽

Random Excitation ◽

Design Parameters ◽

State Function ◽

Fourth Moment ◽

Fatigue Reliability ◽

Random Load ◽

Reliability Sensitivity ◽

Reliability Sensitivity Analysis ◽

Mechanical Components

Fatigue failure is the typical failure mode of mechanical components subjected to random load-time history. It is important to ensure that the mechanical components have an expected life with a high reliability. However, it is difficult to reduce the influence of factors that affect the fatigue reliability and thus a reliability sensitivity analysis is necessary. An approach of fatigue reliability sensitivity analysis of complex mechanical components under random excitation is presented. Firstly, load spectra are derived using a theoretical method. A design of experiment (DOE) is performed to study the stresses of dangerous points according to the change of design parameters of the mechanical component. By utilizing a Back-Propagation (BP) algorithm, the explicit function relation between stresses and design parameters is formulated and thus solves the problem of implicit limit state function. Based on the damage accumulation (DA) approach, the probability perturbation method, the fourth-moment method, the Edgeworth expansion is adopted to calculate the fatigue reliability and reliability-based sensitivity. The fatigue reliability sensitivity analysis of a train wheel is performed as an example. The results of reliability are compared with that obtained using Monte Carlo simulation. The reliability sensitivity of design parameters in the train wheel is analyzed.

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Reliability Sensitivity Analysis Method for Mechanical Components

Mathematical Problems in Engineering ◽

10.1155/2021/7867003 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Yan-Fang Zhang ◽

Yan-Lin Zhang

Keyword(s):

Sensitivity Analysis ◽

Failure Probability ◽

Reduction Method ◽

Dimension Reduction Method ◽

Edgeworth Series ◽

Reliability Sensitivity ◽

Reliability Sensitivity Analysis ◽

Distribution Parameters ◽

Mechanical Components ◽

Univariate Dimension Reduction Method

Based on the univariate dimension-reduction method (UDRM), Edgeworth series, and sensitivity analysis, a new method for reliability sensitivity analysis of mechanical components is proposed. The univariate dimension-reduction method is applied to calculate the response origin moments and their sensitivity with respect to distribution parameters (e.g., mean and standard deviation) of fundamental input random variables. Edgeworth series is used to estimate failure probability of mechanical components by using first few response central moments. The analytic formula of reliability sensitivity can be derived by calculating partial derivative of the failure probability P f with respect to distribution parameters of basic random variables. The nonnormal random parameters need not to be transformed into equivalent normal ones. Three numerical examples are employed to illustrate the accuracy and efficiency of the proposed method by comparing the failure probability and reliability sensitivity results obtained by the proposed method with those obtained by Monte Carlo simulation (MCS).

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