Explore the vast range of titles available.

This paper deals with a continuously degrading turbine runner due to cavitation that is inspected at fixed time intervals. The degradation of the system is modelled with a gamma process. This paper is focused on comparing the influence of maintenance parameters with the long-time cost criterion. A case study, based on simulated degradation paths, shows that there exists a set of parameters that minimize maintenance costs.

The evolution of sensor technologies and real-time data processing has amplified the practicality of incorporating behavioral characteristics within security frameworks. Keystroke dynamics, in particular, has emerged as a prevalent behavioral biometric owing to the ubiquitous use of devices like mobile phones and computers, all reliant on password-based security systems. This study unveils an innovative authentication framework using leveraging deep learning algorithms, tapping into force and displacement data derived from the intricate abduction movements of the right index finger as a distinctive biometric trait. To ascertain its efficacy, we meticulously optimized this novel algorithm while benchmarking it against established deep learning models—Convolutional Neural Networks (CNNs), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), and one-dimensional CNN (1D-CNN). The subsequent evaluation encompassed a comprehensive comparative analysis of their performance metrics. The findings of this evaluation are compelling, demonstrating an average F1 score of 75.5% in validation data alongside an impressive average accuracy rate of 99.4%. These outcomes unequivocally highlight the precision and reliability inherent in utilizing force and displacement patterns as behavioral biometrics. Equally noteworthy is the system's display of a remarkably low False Acceptance Rate (FAR) of 0.27%, positioning it as a promising contender for seamless integration within advanced security systems. In essence, this research not only showcases the potential of leveraging nuanced behavioral traits but also emphasizes the practicality and robustness of employing force and displacement patterns as precise indicators in the realm of behavioral biometrics for enhanced system authentication and security.

Orano Mining is evaluating the potential of the CeBr 3 spectrometric gamma-ray logging probe developed by Advanced Logic Technology (ALT) for estimating uranium concentration in roll-front deposits, where decay chain disequilibrium disrupts its relationship with total gamma count rate. The Nuclear Measurement Laboratory of CEA IRESNE, in Cadarache, France, is working on predictive algorithms capable of fully exploiting the shape of the recorded gamma spectra. However, the current number of logged wells is insufficient to properly train such algorithms. This calls for the creation of a diverse database of simulated gamma-ray logs, using the Monte Carlo N-Particle (MCNP) transport code. Conventional analog simulations based on MCNP pulse-height (F8) tally provide accurate estimates but are too computationally expensive for this task. Achieving low statistical uncertainty in every energy bin necessitates simulating a large number of particle histories. Moreover, modeling borehole measurements involves simulating numerous probe locations. Designing a more efficient yet sufficiently accurate simulation procedure is therefore crucial. We propose a 2-step method, where the flux reaching the probe surface and the detector response to this flux are simulated separately. The bulk of runtime reduction is achieved in the first step, through the use of MCNP point detector (F5) tally, which acts as a variance reduction technique. However, treating the detector as a point may lead to significant bias. We conducted an experimental validation on Orano CIME calibration blocks (Bessines, France) and observed good agreement between measured and simulated spectra. These findings support the 2-step approach as a viable option for building our database.

Recently, maintaining a complex mechanical system at the appropriate times is considered a significant task for reliability engineers and researchers. Moreover, the development of advanced mechanical systems and the dynamics of the operating environments raises the complexity of a system’s degradation behaviour. In this aspect, an efficient maintenance policy is of great importance, and a better modelling of the operating system’s degradation is essential. In this study, the non-monotonic degradation of a centrifugal pump system operating in the dynamic environment is considered and modelled using variance gamma stochastic process. The covariates are introduced to present the dynamic environmental effects and are modelled using a finite state Markov chain. The degradation of the system in the presence of covariates is modelled and prognostic results are analysed. Two machine learning algorithms k-nearest-neighbour (KNN) and neural network (NN) are applied to identify the various characteristics of degradation and the environmental conditions. A predefined degradation threshold is assigned and used to propose a prognostic result for each classification state. It was observed that this methodology shows promising prognostic results.

We consider a repairable system modeled by a semi-Markov process (SMP), where we include a geometric renewal process for system degradation upon repair, and replacement strategies for non-repairable failure or upon N repairs. First Pérez-Ocón and Torres-Castro studied this system (Pérez-Ocón and Torres-Castro in Appl Stoch Model Bus Ind 18(2):157–170, 2002) and proposed availability calculation using the Laplace Transform. In our work, we consider an extended state space for up and down times separately. This allows us to leverage the standard theory for SMP to obtain all reliability related measurements such as reliability, availability (point and steady-state), mean times and rate of occurrence of failures of the system with general initial law. We proceed with a convolution algebra, which allows us to obtain final closed form formulas for the above measurements. Finally, numerical examples are given to illustrate the methodology.

Reaction turbines, of which Francis turbines, constitute a large proportion of low and medium head turbines installed in hydropower plants. Managing these machines represents a real challenge in terms of efficiency, competitiveness and demands on the energy market. Turbines runner blades exhibit loss of performance from damage due to several reasons. One common source of damage is erosion due to the cavitation phenomenon. Indeed, at a given operating region, rapid changes of velocity can create bubbles in the water flow due to local low pressures. When cavitation bubbles reach pressure recovery, they collapse and may induce wear or erosion in these regions. Even if this phenomenon has been intensively studied in the past decades, cavitation erosion is not fully understood as it is driven by several parameters such as flow dynamic, turbine design, environment, or material properties. Some of these parameters can be studied in laboratory to compare materials resistance between each other. This article aims to model the cavitation by a stochastic model using erosion experimental data observed in the laboratory. The benefit of such models is to consider both the uncertainties and natural fluctuations of the phenomenon. With the proposed framework, the study will highlight the differences observed in cavitation erosion experiments of two common materials used to manufacture Francis's runners. This study is the first step in a project aiming at the prediction of turbines mass loss due to cavitation erosion on actual operating Francis turbines.

The paper presents a Bayesian approach of the Brown-Proschan imperfect maintenance model. The initial failure rate is assumed to follow a Weibull distribution. A discussion of the choice of informative and non-informative prior distributions is provided. The implementation of the posterior distributions requires the Metropolis-within-Gibbs algorithm. A study on the quality of the estimators of the model obtained from Bayesian and frequentist inference is proposed. An application to real data is finally developed.

The aim of a condition-based maintenance policy is to manage the available online information about a component or a (sub)system, usually its degradation level, in order to improve the maintenance decision-making. This paper tackles the problem of maintenance decision rules for stochastically deteriorating systems that are subject to changes of their degradation rate during a life cycle. A well-suited control-limit maintenance decision rule is considered with an embedded online change detection algorithm. The maintenance decision and change detection parameters are optimized with respect to the same global maintenance cost and according to the available information about the degradation process. The obtained policy is compared with more classical control limit condition-based maintenance policies without online change detection. The use of the embedded online detection algorithm shows its efficiency especially for systems subject to significantly different degradation rates.

Preventive maintenance planning is an important problem for the handling of energy production systems with high down time costs. Throughout the last decade different maintenance strategies have been developed and optimized in order to minimize operational and maintenance costs whilst conserving and improving the system reliability and productivity. Preventive maintenance strategies are usually based on the monitoring and the prediction of the system behavior and its deterioration process. However, some industrial systems may be operating under a dynamic environment and/or variable working conditions. In this case both the deterioration and the production processes may not be deterministic and incorporate different types of uncertainties. In this paper, we consider the case of a preventive maintenance strategy for a production system subject to uncertainty. For this system, a decision-making procedure for condition-based maintenance planning is proposed. In order to consider uncertainty in production and deterioration processes, these latter are modeled by non-monotonic stochastic processes. The modeling of deterioration processes by means of jump-diffusion stochastic processes has been proposed in our previous work. In this paper, a decision-making approach for preventive maintenance strategies is proposed. Knowing the remaining useful life of a system, a simulation-based real options analysis is used in order to determine the best date to maintain. Considering a case study of a wind turbine with PHM structure, the decision-making approach is described and tested through an empirical example.

Dealing with nuisance parameters is an important issue in online monitoring safety-critical complex systems and detecting events/changes that affect their functioning. A linear stochastic–dynamical system with deterministic nuisance parameters and additive changes is considered in this chapter. The problem of the quickest change detection with nuisance parameters is solved. The peculiarity of the deterministic nuisance parameter rejection in the considered case is the fact that the rejection operator is a function of the size of sliding observation window, which is used to decide between two hypotheses (no fault and there is a fault within a given time window). In the case of sequential change detection this window varies leading to a new and difficult problem of change detection with an unknown dynamic profile of changes.