Reliability Sensitivity-based Correlation Coefficient Calculation in Structural Reliability Analysis

The correlation coefficients of random variables of mechanical structures are generally chosen with experience or even ignored,which cannot actually reflect the effects of parameter uncertainties on reliability.To discuss the selection problem of the correlation coefficients from the reliability-based sensitivity point of view,the theory principle of the problem is established based on the results of the reliability sensitivity,and the criterion of correlation among random variables is shown.The values of the correlation coefficients are obtained according to the proposed principle and the reliability sensitivity problem is discussed.Numerical studies have shown the following results：（1） If the sensitivity value of correlation coefficient ρ is less than（at what magnitude 0.000 01）,then the correlation could be ignored,which could simplify the procedure without introducing additional error.（2） However,as the difference between ρs,that is the most sensitive to the reliability,and ρR,that is with the smallest reliability,is less than 0.001,ρs is suggested to model the dependency of random variables.This could ensure the robust quality of system without the loss of safety requirement.（3） In the case of ｜Eabs｜ρ0.001 and also ｜Erel｜ρ0.001,ρR should be employed to quantify the correlation among random variables in order to ensure the accuracy of reliability analysis.Application of the proposed approach could provide a practical routine for mechanical design and manufactory to study the reliability and reliability-based sensitivity of basic design variables in mechanical reliability analysis and design.

Robust signal recovery algorithm for structured perturbation compressive sensing

It is understood that the sparse signal recovery with a standard compressive sensing（CS） strategy requires the measurement matrix known as a priori. The measurement matrix is, however, often perturbed in a practical application.In order to handle such a case, an optimization problem by exploiting the sparsity characteristics of both the perturbations and signals is formulated. An algorithm named as the sparse perturbation signal recovery algorithm（SPSRA） is then proposed to solve the formulated optimization problem. The analytical results show that our SPSRA can simultaneously recover the signal and perturbation vectors by an alternative iteration way, while the convergence of the SPSRA is also analytically given and guaranteed. Moreover, the support patterns of the sparse signal and structured perturbation shown are the same and can be exploited to improve the estimation accuracy and reduce the computation complexity of the algorithm. The numerical simulation results verify the effectiveness of analytical ones.

A cost driven predictive maintenance policy for structural airframe maintenance

Airframe maintenance is traditionally performed at scheduled maintenance stops.The decision to repair a fuselage panel is based on a fixed crack size threshold,which allows to ensure the aircraft safety until the next scheduled maintenance stop.With progress in sensor technology and data processing techniques,structural health monitoring（SHM） systems are increasingly being considered in the aviation industry.SHM systems track the aircraft health state continuously,leading to the possibility of planning maintenance based on an actual state of aircraft rather than on a fixed schedule.This paper builds upon a model-based prognostics framework that the authors developed in their previous work,which couples the Extended Kalman filter（EKF） with a firstorder perturbation（FOP） method.By using the information given by this prognostics method,a novel cost driven predictive maintenance（CDPM） policy is proposed,which ensures the aircraft safety while minimizing the maintenance cost.The proposed policy is formally derived based on the trade-off between probabilities of occurrence of scheduled and unscheduled maintenance.A numerical case study simulating the maintenance process of an entire fleet of aircrafts is implemented.Under the condition of assuring the same safety level,the CDPM is compared in terms of cost with two other maintenance policies：scheduled maintenance and threshold based SHM maintenance.The comparison results show CDPM could lead to significant cost savings.

Stability Robustness Measures of Linear Discrete-Time Systems with Structured Perturbations

This paper consider the robust stability of linear discrete-time systems subjected toreal structured perturbations. The "zero exclusion principle", which is based on the properties of theKronecker product and the bialternate product, is employed to derive the new robust stability boundsfor time-invariant perturbations. A numerical examples is Presented to demonstrate the merit of theproposed method. The example shows that the new bounds are easy to compute numerically and canhave an arbitrary degree of improvement over the Previous ones reported by the Lyapunov stabilitymethod.

Stochastically Perturbed Parameterizations for the Process-Level Representation of Model Uncertainties in the CMA Global Ensemble Prediction System

To represent model uncertainties at the physical process level in the China Meteorological Administration global ensemble prediction system(CMA-GEPS),a stochastically perturbed parameterization(SPP)scheme is developed by perturbing 16 parameters or variables selected from three physical parameterization schemes for the planetary boundary layer,cumulus convection,and cloud microphysics.Each chosen quantity is perturbed independently with temporally and spatially correlated perturbations sampled from log-normal distributions.Impacts of the SPP scheme on CMA-GEPS are investigated comprehensively by using the stochastically perturbed parametrization tendencies(SPPT)scheme as a benchmark.In the absence of initial-condition perturbations,perturbation structures introduced by the two schemes are investigated by analyzing the ensemble spread of three forecast variables’physical tendencies and perturbation energy in ensembles generated by the separate use of SPP and SPPT.It is revealed that both schemes yield different perturbation structures and can simulate different sources of model uncertainty.When initialcondition perturbations are activated,the influences of the two schemes on the performance of CMA-GEPS are assessed by calculating verification scores for both upper-air and surface variables.The improvements in ensemble reliability and probabilistic skill introduced by SPP and SPPT are mainly located in the tropics.Besides,the vast majority of the reliability improvements(including increases in ensemble spread and reductions in outliers)are statistically significant,and a smaller proportion of the improvements in probabilistic skill(i.e.,decreases in continuously ranked probability score)reach statistical significance.Compared with SPPT,SPP generally has more beneficial impacts on200-hPa and 2-m temperature,along with 925-hPa and 2-m specific humidity,during the whole 15-day forecast range.For other examined variables,such as 850-hPa zonal wind,850-hPa temperature,and 700-hPa humidity,SPP tends to yield more reliable ensembles at lead times beyond day 7,and to display comparable probabilistic skills with SPPT.Both SPP and SPPT have small impacts in the extratropics,primarily due to the dominant role of the singular vectors-based initial perturbations.

Algorithm based on local breeding of growing modes for convection-allowing ensemble forecasting

We propose a method based on the local breeding of growing modes(LBGM) considering strong local weather characteristics for convection-allowing ensemble forecasting. The impact radius was introduced in the breeding of growing modes to develop the LBGM method. In the local breeding process, the ratio between the root mean square error(RMSE) of local space forecast at each grid point and that of the initial full-field forecast is computed to rescale perturbations. Preliminary evaluations of the method based on a nature run were performed in terms of three aspects: perturbation structure, spread,and the RMSE of the forecast. The experimental results confirm that the local adaptability of perturbation schemes improves after rescaling by the LBGM method. For perturbation physical variables and some near-surface meteorological elements, the LBGM method could increase the spread and reduce the RMSE of forecast,improving the performance of the ensemble forecast system.In addition, different from those existing methods of global orthogonalization approach, this new initial-condition perturbation method takes into full consideration the local characteristics of the convective-scale weather system, thus making convectionallowing ensemble forecast more accurate.