A Perturbed Inverse Gaussian Process Model with Time Varying Variance-To-Mean Ratio

International audienceThe inverse gaussian (IG) process has become a common model for reliability analysis of monotonic degradation processes. The traditional IG process model assumes that the degradation increment follows an IG distribution, and the variance-to-mean ratio (VMR) is constant with time. However, for the degradation paths of some practical applications, e.g., the GaAs laser degradation data that motivated to propose the IG process, the VMR is actually time varying. Confronted with this, we propose an IG process model with measurement errors that depend on the actual degradation level. According to different forms or parameter values of the dependence function, the VMR of the degradation paths can display different time varying patterns. The maximum likelihood estimation method is developed in a step-by-step way, combined with numerical integration method and heuristic optimization method. Finally, the GaAs laser example is revisited to illustrate the effectiveness of the proposed model, which indicates that the introduction of statistically dependent measurement error can provide better fitting results and lifetime evaluation performance

Atomically resolved electrically active intragrain interfaces in perovskite semiconductors

Deciphering the atomic and electronic structures of interfaces is key to developing state-of-the-art perovskite semiconductors. However, conventional characterization techniques have limited previous studies mainly to grain-boundary interfaces, whereas the intragrain-interface microstructures and their electronic properties have been much less revealed. Herein using scanning transmission electron microscopy, we resolved the atomic-scale structural information on three prototypical intragrain interfaces, unraveling intriguing features clearly different from those from previous observations based on standalone films or nanomaterial samples. These intragrain interfaces include composition boundaries formed by heterogeneous ion distribution, stacking faults resulted from wrongly stacked crystal planes, and symmetrical twinning boundaries. The atomic-scale imaging of these intragrain interfaces enables us to build unequivocal models for the ab initio calculation of electronic properties. Our results suggest that these structure interfaces are generally electronically benign, whereas their dynamic interaction with point defects can still evoke detrimental effects. This work paves the way toward a more complete fundamental understanding of the microscopic structure–property–performance relationship in metal halide perovskites

Degradation analysis based on an extended inverse Gaussian process model with skew-normal random effects and measurement errors

International audienceAs an important degradation model for monotonic degradation processes, the inverse Gaussian (IG) process model has attracted a lot of attention. To characterize random effects among test samples, the traditional IG process model usually assumes a normal distributed degradation rate. However, the degradation rates in some applications may manifest some asymmetric and non-normal behaviors, such as the GaAs laser degradation data. Therefore, we propose an extended inverse Gaussian (EIG) process model by incorporating skew-normal random effects, and derive its analytical lifetime distribution. Furthermore, considering that available studies about IG process models are limited on the aspect of measurement errors, parameter estimation methods for the proposed degradation model are developed for two scenarios, i.e., the maximum likelihood estimations (MLEs) for perfect measurements, and an extended Monte Carlo (MC) integration algorithm for the MLEs for perturbed measurements. Then a simulation study is adopted to show the effectiveness of the proposed MLEs, and two illustrative examples of GaAs laser degradation and fatigue crack growth are provided to illustrate the advantages of the proposed EIG process model, i.e., the improvement in degradation data fitting performance and lifetime evaluation accuracy by incorporating skew-normal random effects and measurement errors

Condition-based maintenance with imperfect inspections for continuous degradation processes

International audienceA condition-based maintenance (CBM) strategy is now recognized as an efficient approach to perform maintenance at the best time before failures so as to save lifetime cycle cost. For continuous degradation processes, a significant source of variability lies in measure- ment errors caused by imperfect inspections, and this may lead to “false positive” or “false negative” observations, and consequently to inopportune maintenance decisions. To the best of our knowledge, researches on CBM optimization with imperfect inspections remain limited for continuous degradation processes, even though the subject is of practical inter- est for the implementation of a CBM policy. Imperfect inspections are indeed imperfect but still return interesting information on the system degradation level, and making them per- fect can be expensive. Therefore, we analyze the economic performance of a maintenance policy with imperfect inspections, and compare it with the classical policy with perfect inspections to see which policy offers the best benefit in a given situation. Furthermore, a CBM policy with a two-stage inspection scheme is proposed to take benefit of mixing both perfect and imperfect inspections in the same maintenance policy. Through numerical ex- periments and a real case study, it is shown that the policy with imperfect inspections can be better than the classical one, and that the proposed policy with a two-stage inspection scheme always leads to the minimum long run maintenance cost rate

Nonlinear step-stress accelerated degradation modelling considering three sources of variability

International audienceIn the absence of enough run-to-failure data, step-stress accelerated degradation testing (SSADT) is often an attractive alternative way to evaluate the reliability of a product, with the advantage of requiring small sample size and short test time. However, the development of a statistical SSADT model for reliability assessment should take into account different sources of variability in the degradation process that generate uncertainty: 1) temporal variability determining the inherent variability of degradation process over time; 2) unit-to-unit variability in three aspects: degradation rates, initial degradation values, time-points of elevating stress levels; and 3) measurement errors in both covariates and degradation performance. As a contribution towards this aim, a new nonlinear Wiener-process-based SSADT model considering simultaneously nonlinearity and three sources of variability is proposed. Using the proposed SSADT model, the lifetime law of the tested product under normal conditions is derived based on the concept of first hitting time (FHT) of a predetermined failure threshold. Following an approach based on genetic algorithms (GA), a modified simulation and extrapolation method, called GA-SIMEX, is also developed for the model parameter estimation. Finally, a simulation study of fatigue crack length growth is presented to illustrate the implementation of the proposed SSADT model

Learning to Rank Videos Personally Using Multiple Clues

In this paper, we introduce a new learning based video content similarity model. The model leverages on multiple clues on the contents of a video and can be used to rank videos in a personalized way. The key to produce a personalized video ranking is to have a good estimate of pairwise video content similarity, which is realized through meta-learning using a radial-basis function network. Four aspects of a video are considered in deriving the video content similarity in our method. The training data to our model are acquired in the form of user judged preference relationships regarding video content similarities. With the optimized video content similarity estimation obtained by our algorithm, we can produce a personalized video ranking that matches more closely an individual user’s watching interest over a collection of videos. The video ranking results generated by our prototype system are compared with the groundtruth rankings supplied by the individual users as well as rankings by the commercial video website YouTube. The results confirm the advantages of our method in generating personalized video rankings

Personalized Web Content Provider Recommendation through Mining Individual Users ’ QoS

We propose an optimal web content provider recommendation algorithm based on mining QoS (quality of service) information of the Internet. The QoS refers principally to the network bandwidth and waiting time (for a connection to be established). For contents replicated over multiple sites, our algorithm recommends a list of webpages having the desired content and ranked according to their QoSs for any specific user. The recommendation is generated through a data mining procedure based on known QoSs of connections between pairs of computers. Our user QoS mining procedure incrementally constructs a neural network group for QoS prediction based on clustering over the prediction errors. An accompanying decision tree algorithm is then used to select the most appropriate neural network among the neural network group to predict the QoS for a particular user connection. Based on our proposed recommendation algorithm, we have implemented a user-oriented search engine which can identify similar web content providers and make a ranked recommendation based on the prediction over the QoS experienced by individual users. Experiment results have verified that our QoS-based personal web content provider ranking algorithm can indeed produce a recommendation that improves the QoS experienced by individual users