Accuracy Of Reliability Evaluation Research Articles

Few-shot reliability evaluation of tribopairs degradation based on active learning supported generative adversarial network

The high reliability of seawater hydraulic components depends on the tribological properties of key tribopairs. It is impractical to conduct a high number of lifetime tests in a short amount of time due to the limitation of the cost and test time constraints in practical engineering applications, which has a significantly impact on the accuracy of reliability evaluation. To solve this, the Generative Adversarial Network (GAN) is provided as a few-shot learning (FSL) technique for expanding the tribopairs degradation data set, which has the potential to generate high-quality samples. Considering the uncertainty and diversity, an active learning approach is proposed to selecting the most effective generated samples. Finally, reliability is finally evaluated based on Wiener process model via using actual data combine with generated data selected from GAN model. Simulations and friction characteristics experiment of polyether ether ketone (PEEK)/17-4PH stainless steel are applied to validate the proposed method, the results show that the proposed method could generate and choose samples that are highly similar to the real samples, the accuracy of reliability evaluation is effectively improved.

Tensor and Confident Information Coverage Based Reliability Evaluation for Large-Scale Intelligent Transportation Wireless Sensor Networks

Wireless sensor network (WSN) plays an important role in information collection and processing in Intelligent Transportation Systems (ITS) recently. With rapid development of ITS, the size and complexity of WSN is rapidly increasing. Thus, how to evaluate the reliability of large-scale WSN has attracted more and more attention. In this paper, reliability is defined as the probability that the WSN is functional. A confident information coverage (CIC) model-based reliability algorithm (CICRA) is proposed to comprehensively consider coverage reliability and connectivity reliability. Especially, to determine the impact of failing nodes on connectivity in large-scale WSN, a grid clustering connectivity algorithm (GCCA) is proposed to reduce the complexity of computing the connectivity between large-scale wireless sensor nodes, which transforms the connectivity problem between nodes into a grid connectivity problem. In addition, a 3-order tensor modeling is proposed to uniformly represent and model node states, node combinations, and coverage patterns. Furthermore, the network reliability combines coverage reliability and connectivity reliability is evaluated by tensor modeling. Simulation results indicate that the proposed CICRA can improve the network reliability and accuracy of reliability evaluation.

Multi-Angle Reliability Evaluation of Grid-Connected Wind Farms with Energy Storage Based on Latin Hypercube Important Sampling

Aiming to combat the problems of slow speed and poor accuracy of reliability evaluation of the power system in wind farms with energy storage, this paper proposes a method of reliability evaluation based on Latin hypercube important sampling (LHIS). Firstly, we aimed to establish the Latin hypercube important sampling evaluation model by combining the Latin hypercube sampling method with the important sampling method. Secondly, we aimed to optimize the sample probability distribution of the components and conduct hierarchical sampling of the system. Then, the comprehensive risk indicator (CRI) was proposed to evaluate the operational risk and the wind storage generation interrupted energy benefit (WSGIEB) was proposed to evaluate the contribution of the reliability. Finally, simulation experiments were carried out through various power system operation scenarios. The simulation results show that the proposed method is 47% higher than the improving importance sampling method (IM-IS) in evaluation speed and 33% higher than the improving importance sampling method in calculation accuracy.

An improved fused-method using degradation data and lifetime data with the gamma process

The degradation data and lifetime data of products are two important types of reliability data concealed valuable reliability information, which can be used to improve the accuracy of reliability evaluation by fusing these data. However, there are some puzzles with the existing methods based on Bayes theory that the calculation is complicated and could be sensitive to subjectivity in some steps of Bayes methods. Meanwhile, the imbalanced data size can easily result in “imbalanced data” problem. Aiming at solving these problems, this paper examines a phased fusion approach of degradation and lifetime data for products whose degradation can be modelled by the Gamma process, which contained modelling the Gamma process degradation model on basis of degradation data, calculating the point estimation of parameters and updating the failure rate on basis of lifetime data. The product assessment is operated by fitting the Gamma distribution curve and solving the shape parameter and scale parameter. It is proved that the proposed method was more accurate and more efficient in operation than existing methods by the simulation study and example, which also avoided the “imbalanced data” problem.

A data optimization and processing method for complex micro‐electronics system in the IoT environment

With the rapid development of network technologies nowadays, the Internet of Things (IoT) has become a comment trend in most situations. In this trend, the complex micro‐electronics system is the most benefited one. Generally, the traditional evaluation and design of the micro‐electronics system is to establish an approximate model based on the statistical theory with a large error probability. Now, with the benefits of IoT, new methods can be applied. In this paper, we mainly use the IoT enabled Back Propagation (BP) neural network to establish the reliability evaluation and analysis model of micro‐electronic complex system. Firstly, the life cycle of micro‐electronic components and mechanical parts obeys exponential distribution and Weibull distribution in the IoT environment respectively. Then the reliability of each piece under different working time and the overall reliability of the system is calculated according to the failure rate of each part. Secondly, the numerical simulation results show that the error of the reliability value between the BP reliability evaluation model and the basic reliability model is very small, which verifies the accuracy and feasibility of the model, and predicts the reliability of the system. The research shows that this method can predict the overall reliability from the reliability of components, improve the accuracy of reliability evaluation, solve the problem that it is difficult to obtain the analytical expression of distribution model based on statistical theory, and reduce the complexity of the model.

Reliability evaluation of a composite power system in the presence of renewable generations

Power system reliability evaluation plays a vital role in the planning and operation studies by reflecting the system safety level. In this paper, a combination of a non-sequential Monte Carlo simulation (MCS)-based model and an improved Estimation of Distribution Algorithm (EDA) is exploited for evaluating the reliability of the composite power systems considering variability and uncertainty of wind farms (WFs) and Photovoltaic (PV) units. Variability of these resources is defined as the random fluctuation of wind speed and solar irradiation caused by changes in the atmosphere, while their uncertainty results from output power forecast errors. In the proposed model, the states of traditional generating units, transmission lines, WFs, and PV units are constructed using non-sequential MCS. These states can be achieved based on the component failure probability for dispatchable traditional generators and transmission lines along with the Probability Distribution Functions (PDFs) of renewable generations. To enhance the computational efficiency of the MCS in the sampling step, the improved EDA upgraded with the Population-Based Incremental Learning (PBIL) algorithm is employed. The proposed mathematical model for reliability evaluation of composite power system is applied to the IEEE RTS 24-bus system, and numerical studies are performed under several case studies. The simulation results confirm the proficiency of the proposed method to improve the computational efficiency, while the high accuracy of reliability evaluation of the composite power system is attained.

Dynamic performance reliability analysis of rolling linear guide under parameter uncertainty

This paper presents a dynamic reliability analysis method of rolling linear guide considering the uncertainty of geometric parameters. A three degree of freedom (DOF) dynamic motion model of rolling linear guide that considers the complexity of the actual load is established. In order to improve the accuracy of reliability evaluation, interval model is used to express the uncertainty of geometric parameters. The reliability range of dynamic response is determined according to the accuracy grade of rolling linear guide. The interval reliability calculation method is used to analyze and calculate the horizontal and vertical dynamic reliability of rolling linear guide. Then, the comprehensive dynamic reliability of rolling linear guide is obtained. Finally, the dynamic reliability of the SHS-45R rolling linear guide is analyzed by using the reliability evaluation method described in this paper. And the validity and accuracy of the result is demonstrated by comparing with the Monte Carlo simulation.

Reliability-based optimisation for offshore structures using saddlepoint approximation

Offshore structures have a long service life and involve high cost. However, the working environment of offshore structures is harsh. When an accident occurs, it causes an adverse social impact and economic loss. Therefore, in the design process of offshore structures, in order to enhance their safety, this is an important problem that needs attention. Reliability-based design and optimisation (RBDO) using saddlepoint approximation (SA) is a powerful method to enhance the reliability of offshore structures. The SA utilises a cumulant generating function and saddlepoint to approximately express the probability density function and cumulative distribution function of a limit-state function. To further improve the accuracy of reliability evaluation in RBDO with SA, in this study, a mean-value second-order SA (MVSOSA) is introduced. A RBDO with MVSOSA strategy is proposed for the design problem of a wellhead platform. An engineering case study illustrates the application of the proposed method.

Study on prior information fusion method of low-voltage switchgear

The reliability analysis of low-voltage switchgear is of great importance to the safety of power system. Bayes theory is a widely used reliability evaluation method because it can make full use of historical data. Therefore, the reliable prior information is the key factor to ensure the correctness of the reliability analysis results when the Bayes method is used to evaluate the reliability of the low-voltage switchgear. In order to obtain the priori information of low-voltage switchgear with high reliability, a feasible prior information fusion method was proposed and a reliable prior distribution function was obtained. First, kinds of historical data and effective information were collected from manufacturers and users. Then, to get more accurate prior distribution, the parametric and nonparametric test method was used to test the compatibility of the collected historical data, the effective information and the field information. Finally, the prior distribution and the expert experience were fused, and the conjugate prior distribution of the reliable low-voltage switchgear was constructed, which provided the theoretical basis for improving the accuracy of reliability evaluation of the low-voltage switchgear.

Reliability-Based Multidisciplinary Design Optimization Using Subset Simulation Analysis and Its Application in the Hydraulic Transmission Mechanism Design

The Monte Carlo simulation (MCS) can provide high reliability evaluation accuracy. However, the efficiency of the crude MCS is quite low, in large part because it is computationally expensive to evaluate a very small failure probability. In this paper, a subset simulation-based reliability analysis (SSRA) approach is combined with multidisciplinary design optimization (MDO) to improve the computational efficiency in reliability-based MDO (RBMDO) problems. Furthermore, the sequential optimization and reliability assessment (SORA) approach is utilized to decouple an RBMDO problem into a sequential of deterministic MDO and reliability evaluation problems. The formula of MDO with SSRA within the framework of SORA is proposed to solve a design optimization problem of a hydraulic transmission mechanism.

The Preprocessing Method for Software Reliability Failure Data

The software reliability failure data is the foundation of the software reliability’s quantitative evaluation based on the failure data, and it has an important influence on the accuracy of reliability evaluation. But there are always noises in the original software reliability failure data and make the reliability evaluation accuracy affected. This paper put forward the collecting method of reliability failure data and data preprocessing method including data cleaning and data analysis method, which based on the analysis of the importance and the source of failure data in the software reliability testing and the classification of software failure data. Finally through an example, it displayed the reduction of data noises and the promotion of data quality which produced by the preprocessing methods.

Maintenance Decision Based on Data Fusion of Aero Engines

Maintenance has gained a great importance as a support function for ensuring aero engine reliability and availability. Cost-effectiveness and risk control are two basic criteria for accurate maintenance. Given that aero engines have much condition monitoring data, this paper presents a new condition-based maintenance decision system that employs data fusion for improving accuracy of reliability evaluation. Bayesian linear model has been applied, so that the performance degradation evaluation of aero engines could be realized. A reliability evaluation model has been presented based on gamma process, which achieves the accurate evaluation by information fusion. In reliability evaluation model, the shape parameter is estimated by the performance degradation evaluation result, and the scale parameter is estimated by failure, inspection, and repair information. What is more, with such reliability evaluation as input variables and by using particle swarm optimization (PSO), a stochastic optimization of maintenance decision for aircraft engines has been presented, in which the effectiveness and the accuracy are demonstrated by a numerical example.

Workload-aware Reliability Evaluation Model in Grid Computing

To address the issue of reliability evaluation in dynamic grid environment, a workload-aware reliability evaluation model is proposed, in which queuing system is applied to describe the dynamic workload and working of grid resources. Meanwhile, a new type of resource fault, namely Deadline-Miss fault, is introduced to evaluate the reliability of real-time jobs. The validity of the proposed model and its approach to calculate real-time job’s reliability are presented theoretically. Extensive experiments are conducted to verify its performance, and the results show that the proposed model can significantly improve the accuracy of reliability evaluation in presence of dynamic workload. Also, scheduling algorithm based on this model can reduce the jobs’ mean response time and the deadline-miss rate.

Reliability Evaluation on Machining Center Based on Three-Parameter Weibull Distribution

In order to improve the model accuracy of reliability evaluation, the Three-Parameter Weibull Distribution model of time between fault was established by introducing location parameters. The correlation coefficient optimization method based on the adaptive genetic algorithm was firstly applied to estimate the location parameter of the Three-Parameter Weibull Distribution. Shape parameter and scale parameter were obtained by the least square method. The time between failures of these series machining center submitting to three-parameter weibull Distribution was checked by the test hypothesis of goodness-of-fit distribution. Finally, the machining center was carried out reliability evaluation based on the Three-Parameter Weibull Distribution model.