Papers by Keyword: Common Cause Failure

Abstract: Redundancy allocation (RA) is one of the important approaches to improve system reliability by incorporating generally s-identical and s-independent components. However in the presence of common cause failures (CCF), a rarely studied problem as Redundancy optimization, the optimized system reliability may be adversely affected. In this paper the effect of CCF on Reliability Optimization using Redundancy Allocation has been expedited. The paper is implemented in two steps. Step1 involves obtaining the optimized solution of the problem using existing techniques. In the next step, modifications are done to compute optimized system reliability under CCF. The optimized reliability expression is modified to depict the effect of CCF. The work is extended to complex network analysis & implicit method has been used to do this work. The adverse effect of CCF on system reliability has also been demonstrated quantitatively with the help of example.

Abstract: Basic principle and methods of Go-Flow methodology have been introduced. In light of the characteristics and analysis demands for CNC machine tools, methods and procedures to evaluate the reliability of CNC machine tools have been put forward.

Abstract: In the general reliability analysis of series systems, the components which compose the system are assumed independent of each other. However, for most mechanical series systems, dependent failure is their common feature. If neglecting dependent failures where CCF(Common Cause Failure) plays an important role, it will lead to considerable errors or even misleading conclusions. In this article, the concept of series chain model is put forward in consideration of the CCF. By using the load—strength interference model, the order statistic theory and the knowledge of statistical distribution, the author develops a specific method to evaluate the reliability of series systems under exponential distribution. In the last part, by using Monte Carlo simulation, a group of specific data are given to verify the series chain model and compare the two different series system model. The results show that the reliability calculation under the classic series model is relatively conservative, while the reliability calculation under the series chain model is convincing. And thus, the series chain model is more applicable in engineering.

Abstract: Basic principle and inference algorithm of Bayesian network (BN) were emphasized. As to the Fault Tree with basic events of common cause failure, accident probability cannot be computed correctly. An open press blunt hand accident risk assessment BN model was set up, in addition, the BN nodes of common cause failure were illustrated and the accident probability of occurrence was obtained. The discussions about computed results indicate that regarding to system with unit events of common cause failure or interdependent events, result of accident probability computed from a BN model is higher than that computed from the Fault Tree/Event Tree model.

Abstract: With the scenario of reliability estimation, a geometrically complex mechanical/structural component with multiple damage sites should be treated as a system, since there are many links (damage sites) of similar failure probabilities on any of such a component and the failures of the individual damage sites are not perfectly dependent of each other. Conventional system reliability model is not applicable to such a system because of the statistical dependence among the element (damage site) failures. To estimate the reliability of a mechanical system (or a complex component) in which element (damage site) failure dependence plays an important role, a model capable of reflecting the effect of element failure dependence is necessary. The present paper develops models which can deal with multiple damage sites and multiple failure mechanisms, reflect the dependence among element failure events and that among different failure modes. Such models are applicable to both typical mechanical systems and various components.

Abstract: Most current common cause failure models are static models in which failure rate is assumed constant. The assumption is unrealistic in numerous situations, especially for mechanical systems. Based on the idea of shock models, this paper modifies unreasonable assumptions in conventional models and describes common cause failure process with non-homogeneous Poisson process, power-exponential process. A dynamic model is obtained. The parameters are estimated with neural network. The model presented in this paper, which is based on time-related power-exponential failure rate like bathtub curve, has more advantages and wider application than the model based on constant failure rate. Examples are given to illustrate its feasibility and precision of computation.