Search

Prediction of event such as failure event or critical warning event plays an important role in reliability engineering. In this paper, we present a semiparametric method to predict event occurrences for an individual unit in real time using recurrent event data. The intensity of event occurrence is modeled using the extended Cox proportional-hazard model and the distinction of units is achieved using an additional frailty parameter. The method presented features an online updating scheme, and therefore, can provide the real-time prediction of the occurrence of next event. We demonstrate the efficacy of frailty and the updating scheme through comprehensive numerical experiments and a case study based on real-world data. [ABSTRACT FROM AUTHOR]

Many systems are composed of spatially distributed units which are subject to different operating conditions. The reliability of such systems depends not only on the reliability of individual units but also on their configurations. In this paper, we develop a degradation model for systems where units are spatially distributed and balanced. More specifically, we consider k-out-of-n pairs:G Balanced systems. The effect of operating conditions on the units is considered and the corresponding reliability estimate is obtained. The degradation path of every unit is modeled based on collected observations of the degradation indicators and its physics or statistics degradation rate. We investigate the effect of the system configuration on the overall system reliability. We also estimate the pdf of time to a specified failure. [ABSTRACT FROM AUTHOR]

Software defect prediction (SDP) is an available way to enhance test efficiency and guarantee software reliability. However, there are more clean instances than defective instances in real software projects, and this results in severe class distribution skews and gets the poor performance of classifiers. So solving the class-imbalance problem in SDP has attracted growing attention from industry and academia in software engineering. In this paper, we propose a novel class-imbalance learning approach for both within-project and cross-project class-imbalance problem. We utilize the thought of stratification embedded in nearest neighbor (STr-NN) to produce evolving training datasets with balanced data. For within-project, we directly employ the STr-NN approach for defect prediction. For cross-project, we first introduce transfer component analysis to mitigate the distribution differences between source and target dataset, and then employ the STr-NN approach on the transferred data. We conduct experiments on PROMISE and NASA datasets using ensemble learning based on weight vote. Experimental results indicate that our approach has higher area under curve (AUC), Recall and comparable probability of a false alarm (pf), and F-measure than some existing methods for the class-imbalance problem. [ABSTRACT FROM AUTHOR]

The applications of k-out-of-n pairs:G Balanced systems with spatially distributed units are emerging in aerospace and military industries. A k-out-of-n pairs:G Balanced system has n pairs of units distributed evenly in a circular configuration. The system operates when at least k pairs of units operate in a balanced arrangement. The reliability estimation of such systems is important since their failures are most likely to result in losses in property and humans such as the case of unmanned aerial vehicles (UAVs) and balanced engine systems in planetary descent vehicles. In this paper we present methods for reliability estimation of different types of k-out-of-n pairs:G Balanced systems in two scenarios: 1) unbalanced systems are considered as failed and 2) unbalanced systems are rebalanced. We develop a systematic approach for enumerating the complete set of successful events, which are ordered sequences of failures described by system state transition paths, and obtain closed form expressions for calculating the probabilities of successful events. The developed methods can be easily generalized to other systems with spatially distributed units. It is found that there exists an optimal redundancy configuration for k-out-of-n pairs:G Balanced systems when unbalanced systems are considered as failed; and that system reliability can be increased by rebalancing unbalanced system. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the reliability and availability analysis of Web service (WS) compositions, orchestrated via the Business Process Execution Language (BPEL). Starting from the failure profiles of the services being composed, which take into account multiple possible failure modes, latent errors, and propagation effects, and from a BPEL process description, we provide an analytical technique for evaluating the composite process' reliability-availability metrics. This technique also takes into account BPEL's advanced composition features, including fault, compensation, termination, and event handling. The method is a design-time aid that can help users and third party providers reason, in the early stages of development, and in particular during WS selection, about a process' reliability and availability. A non-trivial case study in the area of travel management is used to illustrate the applicability and effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Many real-world critical systems, such as aircraft and human space flight systems, utilize mission aborts to enhance the survivability of the system. Specifically, the mission objectives of these systems can be aborted in cases where a certain malfunction condition is met, and a rescue or recovery procedure is then initiated for system survival. Traditional system reliability models typically cannot address the effects of mission aborts, and thus are not applicable to analyzing systems subject to mission abort requirements. In this paper, we first develop a numerical methodology to model and evaluate mission success probability and system survivability of 1-out-of-N warm standby systems subject to constant or adaptive mission abort policies. The system components are heterogeneous, characterized by different performances and different types of time-to-failure distributions. Based on the proposed evaluation method, we make another new contribution by formulating and solving the optimal mission abort problem, as well as a combined optimization problem that identifies the mission abort policy and component activation sequence maximizing mission success probability while achieving the desired level of system survivability. Efficiencies of constant and adaptive mission abort policies are compared through examples. Examples also demonstrate the tradeoff between system survivability and mission success probability due to the utilization of a mission abort policy. Such a tradeoff analysis can help identify optimal decisions on system mission abort and standby policies, promoting safe and reliable operation of warm standby systems. [ABSTRACT FROM PUBLISHER]

In engineering scenarios, expert judgments play an essential role in reliability assessment, especially for those systems with few historical data. To achieve a rational result, experts from different areas should be involved, and the uncertainties in their assessments should be properly addressed. Such information is often referred to as multisource imprecise information (MSII) and might contain high degree of conflicts, as different experts usually have different expertise and knowledge. Properly quantifying the conflicts among the MSII, then, becomes a critical issue, as the subsequent processing of MSII (e.g., combination and calibration), depends on the degree of conflict in the MSII. To this end, a new conflict measure is put forth based on the Dempster–Shafer theory (DST) to quantify and visualize the conflict in the MSII from a group of experts. In the first place, the MSII from each expert is used to construct the basic belief assignment (BBA) of the reliability estimates for the corresponding expert under the DST. A 2-D conflict measure, which combines the conflict factor and Jousselme distance in DST, is, then, proposed to measure the conflict between the experts’ BBAs. The conflict is quantified from two perspectives, viz., mutual conflict and total conflict. Finally, a Bhattacharyya distance-based method is developed to further quantify the informativeness of each expert's MSII to the system reliability estimate. A numerical example along with an engineering case is used to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Gas-path analysis (GPA) method has been widespreadly used to monitor gas turbine engine health status, and has become one of the key techniques in favor of condition-oriented maintenance strategy. GPA method (especially nonlinear GPA) can easily obtain the magnitudes of the gas-path component faults. Usually, it is essential to use correct measurement information to obtain correct fault signature for producing accurate gas-path diagnostic results. However gas-path components as well as sensors may degrade or even fail during gas turbine operations. The degraded sensors may produce significant measurement biases, which do not follow the Gaussian distribution, and misleading diagnostic results may be obtained. In order to solve this problem, a method to improve the robustness of gas turbine gas-path fault diagnosis against sensor faults was proposed for the typical nonlinear GPA method. The proposed method includes two steps: first, to locate suspicious degraded sensors based on Gaussian data reconciliation principle for all the gas-path measurements and second, to detect, isolate, and quantify the degradation rate of major gas-path components based on an extended nonlinear GPA method. The proposed method can effectively and accurately detect and isolate degraded gas-path components as well as sensors, and further quantify the component degradations. [ABSTRACT FROM PUBLISHER]

Some practical systems such as blast furnaces and turbofan engines have degradation processes with memory effects. The term of memory effects implies that the future states of the degradation processes depend on both the current state and the past states because of the interaction with environments. However, most works generally used a memoryless Markovian process to model the degradation processes. To characterize the memory effects in practical systems, we develop a new type of degradation model, in which the diffusion is represented as a fractional Brownian motion (FBM). FBM is actually a special non-Markovian process with long-term dependencies. Based on the monitored data, a Monte Carlo method is used to predict the remaining useful life (RUL). The unknown parameters in the proposed model can be estimated by the maximum likelihood algorithm, and then the distribution of the RUL is predicted. The effectiveness of the proposed model is fully verified by a numerical example and a practical case study. [ABSTRACT FROM PUBLISHER]