For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve the precision of the system reliability assessment. If the multi-level pass/fail data are overlapping,one challenging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps： fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and finally simplifying the likelihood function. An example of a satellite rolling control system demonstrates the feasibility and the efficiency of the proposed approach.
With more and more attention on degradation process, we need the degradation model to be accurate all over the time rather than only at some specific moments. However, the traditional validation metric only estimates difference of static features. A validation method proposed in this paper uses hypothesis testing to identify whether the distributions of experimental measurements and simulation results are consistent. Then, based on the deviation between sample means, a global validation metric which reflects the difference of degradation process between computational model and physical system all over the service time is derived from the statistics of deviation between sample means. Furthermore, curve fit method for discrete experimental measurements is introduced. The case of electro-hydraulic servo valve is studied, and the results show that the proposed validation metric is appropriate for the validation of degradation model with dynamic performance output.
Electromechanical product's reliability is affected by uncertainty as well as performance degeneration during its life cycle.The present reliability and performance integrating modeling methods have obvious deficiencies in long period reliability analysis and assessment when applied to such system.A novel integrating modeling method based on physics of failure(PoF)and a simulation algorithm that considers uncertainty and degeneration are proposed in this paper to compute maintenance free operation period or maintenance free operation period survivability which is used to assess long period reliability of system.Furthermore,the concept design of this kind of software based on the above theory is also introduced.A case study of servo valve demonstrates the feasibility of the method and usability of the software in this research.
Traditionally,parameter design is carried out prior to tolerance design. However, this two-step design strategy cannot guarantee optimal robustness for products' quality. The proposed integrated robust design method determined the optimal parameter and tolerance simultaneously by calculating the maximum tolerance region,thereby improving the quality of products. In addition,the proposed method did not need uncertainty analysis to obtain the maximum tolerance region,so that the calculation cost could be decreased. And the method avoided the difficulty of gaining costtolerance function as maximum tolerance region represented both demand of cost and robust. Finally,an amplifier circuit case was conducted for verification purpose. Based on the results, the proposed approach could provide robust solution with optimal maximum tolerance region.
