One of the key challenges in reliability estimation is the acquisition of failure information especially under real-life scenarios or computationally expensive long-run simulations. Due to the lack ...of failure information, it becomes a challenging task to estimate time-dependent reliability of a structure or component. In this paper, a preliminary study of time-dependent reliability prediction using transfer learning is given by utilizing only current time performance function information. Transfer learning, specifically domain adaptation, is used in this work in order to find a representation in latent space where the statistical properties of nonstationary stochastic processes such as variance are preserved and their distribution parameters at different times are made close to each other. Correlated random samples of the stochastic processes are generated in present and future time intervals in order to find common latent space using domain adaptation via transfer component analysis (TCA). Due to the excellent computational efficiency, a Kriging surrogate model is built in the present interval only using an adaptive sampling strategy. The reliability in the future is predicted using the same surrogate model without retraining it using future performance function information. Monte Carlo simulation (MCS) is used to validate the results of the proposed method. The results show that the proposed method can predict the failure probability of time-dependent problems efficiently with significant accuracy.
It is a challenging task to effectively estimate the upper bound as well as the lower bound of failure probability under epistemic and aleatory uncertainties in a dynamic environment. This paper ...proposes a novel approach to estimating both the lower and upper bounds of the time-variant hybrid failure probability by combining the proposed box moments and improved maximum entropy methods. The sparse grid sampling technique is first employed to simplify the time-variant hybrid performance function for the integration. A new continuous maximin algorithm based on the improved particle swarm optimization is then proposed for estimating the box moments. The probability density function and the time-variant hybrid failure probability can be finally estimated with the improved maximum entropy method by introducing the nonlinear transformation with an adjustable parameter. Two engineering examples are employed to testify the effectiveness of the proposed method.
Due to dynamic uncertainties presence in service and performance conditions, time-dependent reliability prediction of a component or structure is a challenging problem. In this research, a transfer ...learning-based technique is proposed to predict the reliability in the future. The complete time interval is divided into two sub-intervals namely, present interval and future interval. It is assumed that the performance function information is available for the present interval only. Transfer learning, specifically domain adaptation is used to transform the stochastic processes to be represented in a way that their sample spaces in different time durations are made closer while maintaining some of their statistical properties such as variance. In order to transform the stochastic processes, correlated samples of stochastic processes are generated using a space-filling sampling technique for the complete time interval. An adaptive Kriging surrogate model is then built using the performance information available for the present interval only using transformed stochastic process samples. The built Kriging model is employed to estimate and predict the reliability for present and future intervals without retraining it using future data. Results show that the proposed method can predict the failure probability in present and future intervals accurately with significant efficiency improvement.
Time-variant reliability analysis can effectively estimate the safe state of structures under dynamic uncertainties during their lifecycle. However, one of its key challenging issues is computational ...efficiency. To improve the efficiency, this paper develops an approximation of the first-crossing probability density function (PDF) method, termed AFC-PDFM, based on the first-crossing theory. The response surface of the first-crossing time point (FCTP) about input variables is first estimated by using the moving least squares method (MLSM) and uniform design. Furthermore, an iterative algorithm is developed to compute the origin moments of the FCTP based on the MLSM response surface. Finally, the PDF of the FCTP is obtained by combining the origin moments and the maximum entropy method to avoid direct calculations of the first-crossing rate, which is often intractable, and then the time-variant reliability is estimated. Three examples are implemented to testify and validate the effectiveness of the proposed method and potential applications to engineering problems.
Flapping wing micro aerial vehicles (FWMAVs) have attracted more attention during the development of the robotic systems field. The size of the flapping wing plays an important role in the lift force ...and torque generation based on quasi-steady aerodynamic model. Therefore, it is necessary to study energy-efficient design methods for wings to provide sufficient lift force and torque with minimal energy consumption for hovering flight. In this paper, the sensitive parameters for the lift force and power consumption were first selected based on design of experiment (DOE) and the parameter of the distributed wing stiffness was determined based on experimental data. Design optimization models for three different cases were then built by considering the lift force as one constraint and the energy consumption as the objective function. The combination of subset simulation and the gradient-based optimization was finally used for solving design optimization models, and the corresponding sensitivity analysis was provided.
It is a challenge to explore the unsteady vortex structures of flexible flapping wings of X-shaped flapping-wing micro air vehicles (also known as clapping-wing micro air vehicles (CWMAVs)). The ...objective of this paper is to obtain the influence of wind speed, flapping frequency, and angle of attack (AoA) on the instantaneous and average force coefficients of CWMAVs, investigate flow visualization of the leading-edge vortex (LEV), trailing-edge vortex (TEV) and wake vortex (WV) structures and identify some novel flow mechanisms of flapping propulsion. This paper proposes a mechanics and particle image velocimetry (PIV) measuring platform in a low-speed tunnel. In addition, cross-correlation peak analysis and kriging image reconstruction are applied for postprocessing to enhance PIV image recognition. Combining the time evolution of the forces, the force coefficients, and the flow visualization, we find the evolution and effects of LEV, TEV and WV structures.
It is a challenging task to effectively control multi-input and multi-output (MIMO) discrete time-varying linear systems. This paper proposes a swarm intelligence based model predictive control (MPC) ...strategy for addressing the challenge. First, a swarm intelligence based iterative dynamic optimal control solver is proposed to avoid the difficulty in solving the algebraic Riccati equation of finite-horizon optimal state control problem. Then, a swarm intelligence based online optimal controller is designed based on the MPC strategy, which can extend the optimal control problem from the finite-horizon to the infinite-horizon. Finally, the feedback structure of the online optimal state control system for MIMO discrete time-varying linear systems is constructed. A real-time simulation and a practical control experiment of a first order inverted pendulum system are employed to elborate the proposed method. The results show that the proposed method has the high efficiency, high accuracy, and anti-interference capability.
In the modern era of digital growth, Data centres serve as the important infrastructure of the interconnected global network. The Data centres perform as central hubs where immense volumes of data ...are processed, stored, and distributed. Reliable performance of data centres and Computer Room Air-Conditioning (CRAC) system is of critical importance, through which optimal environmental conditions are achieved. This paper proposes an interesting approach to evaluate the CRAC system’s ability to function properly under various uncertainties. Heat transfer model is developed using various environmental parameters, data server rack parameters, and evolving capacity of data servers. The uncertainties are modelled as random variables. One of the challenging issues in estimating the reliability of a system under uncertainties is to reduce the computational requirements while maintaining the accuracy. To overcome the issue, Kriging model is developed using adaptive sampling. The proposed approach is compared with the available approaches. The method shows better computational efficiency and accuracy.
Performance of complicated products usually changes over time and space at the same time and meanwhile multiple failure modes exist, which enables designers to encounter time- and space-variant ...system reliability (TS-VSR) measures during the product design and development stages. This paper proposes a general decoupling approach to solving the time- and space-variant system reliability-based design optimization (TS-VSRBDO) problem, which aims at providing an effective tool for reliability-based design of complicated products. For solving the TS-VSR constraints of the TS-VSRBDO problem, an enhanced simulation-based method is firstly proposed, and then the mapping between design variables and TS-VSR constraints is established by combining exponential function approximation with moving least squares method in each cycle. Besides, based on the equivalent deterministic optimization, an iteration mechanism is constructed to ensure the convergence of the whole optimization procedure. The TS-VSRBDO problem is finally decoupled into a series of TS-VSR analysis and deterministic optimization. Several examples are investigated to demonstrate the effectiveness of the proposed method.
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•Time- and space-variant system reliability-based design optimization is defined.•An enhanced simulation-based method is proposed.•A decoupling iterative algorithm is proposed.•Accuracy and efficiency of the proposed method have been validated via five cases.
Product performance usually degrades with time. When shocks exist, the degradation could be more rapid. This research investigates the reliability analysis when typical degradation and shocks are ...involved. Three failure modes are considered: catastrophic (binary state) failure, degradation (continuous processes), and failure due to shocks (impulse processes). The overall reliability equation with three failure modes is derived. The effects of shocks on performance are classified into two types: a sudden increase in the failure rate after a shock, and a direct random change in the degradation after the occurrence of a shock. Two shock scenarios are considered. In the first scenario, shocks occur with a fixed time period; while in the second scenario, shocks occur with varying time periods. An engineering example is given to demonstrate the proposed methods.
