Computer Aided Engineering driven analysis is gaining grounds in automotive industry. Prediction of brake noise using CAE techniques has become populardue to its overall low cost as compared to physical testing. However, the presence of several uncertain parameters which affect brake noise and also the lack of basic understanding about brake noise, makes it difficult to make reliable decisions based on CAE analysis. Therefore, the confidence level in CAE techniques has to be increased to ensure reliability and robustness in the CAE solutions which support design work. One such way to achieve reliability in the CAE analysis isinvestigated in this thesis by incorporating the effects of different sources of uncertainty and variability in the analysis and estimating the probability of designfailure (probability of brake noise above a certain threshold). While incorporating the uncertainties in the CAE analysis ensures robustness, it is computationally intensive. This thesis work aims to gain an understanding about a brakenoise - creep groan, and to bring robustness into the CAE analysis along with reduction in computational time. A probabilistic analysis technique called hierarchical multi-fidelity statistical approachis explored in this thesis work, to estimate the probability of design failure or design robustness at a faster rate. It incorporates the stochasticity in the input parameters while running simulations. The method involves application of a hierarchy of approximations to the system response computed with variations in mesh resolution or variations in number of modes or changing solver time step,etc. And finally it uses the probability theory, to relate the information provided by approximate solutions to get the target failure estimation.Through this method, reliable data regarding the probability of design failure was approximated for every simulation and at a reduced computational time.Additionally, it provided information about critical parameters that influenced brake noise which was meritorious for design management. Estimation of probability of design failure by this method has been proved to be reliable in the case of brake noise according to the simulation results and the method can be considered robust. / Computer Aided Engineering (cae) driven analysis is gaining grounds in automotive industry. Brake noise is one such place where cae simulations are gaining more attention. The presence of several uncertain parameters which affect brake noises and also the lack of basic understanding about brake noise, makes it difficult to make reliable decisions based on cae deterministic analyses alone.Therefore, the confidence level in cae analyses has to be increased to ensure cae analysis robustness. One way to achieve this is by incorporating the effects of different sources of uncertainty and variability in the cae analysis and estimating the probability of design failure. Such a reliability measure (i.e. probability of noise event occurrence or exceedance of noise level than a threshold) can provide car manufacturers with an idea about the costs of warranty claims due to brake noise and can be used as a metric to evaluate different design solutions, before the final design goes to the production stage.  On one hand, using the high-fidelity models of brake/chassis system is generally computationally intensive, and thus, often only limited number of simula-tion runs are feasible for uncertainty analysis and design failure risk assessment. On the other hand, analyses on low-fidelity models, typically based on simplified assumptions during the development phase are fast but not always accu-rate. Striking for a good balance between efficiency and accuracy/robustness is an important task, when dealing with uncertainty/risk analysis of such complex dynamical systems To address these issues, a hierarchical multi-fidelity statistical approach has been adopted in this study, in order to estimate the probability of design failure. It employs a hierarchy of approximations to the system response computed with different fidelity by surrogate modelling, coarse spatial/temporal model mesh resolution variation, changing solver time step, etc., using probability theory, to relate information provided by approximate solu-tions to the target failure estimation. Using this approach opens up the possi-bility to use a low-fidelity models to accelerate the uncertainty quantification of complex brake/chassis systems, while granting unbiased estimation of system design failure risk/reliability. It also enables management of design changes, during fast iterations of the design process. This approach is used for studying one of the brake noise issue called creep groan, understand the root cause and providedesign proposals.