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In this paper, we propose an adaptive hybrid age/condition-based maintenance policy for units whose degradation paths can be modelled via a gamma process with random effect. The maintenance policy consists in measuring the degradation level of the unit at a first (age-based) inspection time, and in using a condition-based rule to decide whether to immediately replace the unit or to postpone its replacement to a future time that is determined, unit by unit, based on the outcome of the inspection. The optimal maintenance policy is defined by minimizing the long-run average cost rate. After each replacement the unit is considered as good as new. The lifetime of the unit is defined by using a failure threshold model. Maintenance costs are computed accounting for preventive replacement cost, corrective replacement cost, inspection cost, logistic cost, and downtime cost (which depends on the time spent in a failed state).

Most of the recent research in the field of maintenance is focused on using more and more information about the state of the system and its environment for predicting future events and making prescriptions about maintenance and operations. Indeed, these prescriptions take the form of recommendations that do not only describe what, how, and when to conduct the maintenance but also consist in advice on how to adjust the system operating conditions for the desired outcome. By following this general idea, this paper suggests a new maintenance policy for a degrading unit that generalizes a policy recently proposed in the literature by including the possibility of influencing the remaining useful life of the unit by changing its usage rate. In fact, this policy assumes that an inspection is performed at a prefixed time and that, based on the result of the inspection, it is decided whether to immediately replace the unit or to postpone its replacement to a second predetermined time and possibly adjust its working rate, if this latter option is deemed convenient. After each replacement the unit is considered as good as new. The degradation process of the unit is described by using a gamma process based model. The unit is assumed to fail when its degradation level passes an assigned threshold. It is supposed that failures are not self-announcing and that failed units can continue to operate, though with reduced performance and/or additional costs. Maintenance costs are computed considering the cost of a preventive replacements, corrective replacements, inspections costs, logistic costs, downtime costs (which account for time spent in a failed state), and costs that account for the change of the unit working rate. This latter cost also includes the possible penalty determined by failure to comply with contract clauses. The optimal maintenance policy is defined by minimizing the long-run average reward rate.

We propose a criticality-based scheduling model for aircraft component replacements.We schedule maintenance for a fleet of aircraft, each equipped with a multi-component system. The maintenance schedule takes into account a limited stock of spare components and the Remaining-Useful-Life prognostics for the components. We propose a component replacement scheduling model with three stages of maintenance criticality: i) critical aircraft that are not airworthy due to a lack of sufficient operational components, ii) predictive alerts for expected component failures, and iii) non-critical aircraft with some failed components. An Adaptive Large Neighborhood Search (ALNS) algorithm is developed to solve this criticality-based aircraft maintenance planning problem. The framework is illustrated for a fleet of aircraft, each equipped with a k-out-of-N system of components. A predictive maintenance planning is obtained within an outstanding computational time (less than 6 seconds for a fleet of 50 aircraft). Moreover, it is shown that the proposed planning with 3-levels of criticality ensures aircraft airworthiness while making cost-efficient use of maintenance slots.

Traffic counts collect information that is valuable, for example, in bridge and road design or maintenance processes. The average daily traffic volume is often the most collected measure of vehicular traffic, which is used in the design or assessment of major highways. Permanent control stations, situated in key locations of the highway network, gather data the entire year. However, one of the disadvantages of traffic count data is that most counters used, do not measure total vehicle weight and axle load data. Traffic counts display only the classification of vehicles, traffic volume, average daily traffic, and annual average daily traffic. Axle loads on the other hand are required, for example, as input in the design of pavement and new bridges, and the reliability assessment of existing ones. Weigh-in-motion (WIM) systems are usually used to collect vehicle load data. The State of Mexico (in central Mexico) has 115 permanent vehicle counting stations with 745 traffic counting points in its federally administered road network. However, due to the lack of WIM stations, it is not possible to obtain axle load data. In this paper, a Bayesian Network (BN) quantified with data from WIM stations in the Netherlands is used to describe the weight and length distribution of heavy vehicles registered in the permanent vehicle counting stations of the State of Mexico federal highways. The Dutch and Mexican vehicle types are matched according to similar characteristics. Later, synthetic WIM observations from the BN model are analysed through extreme value theory and vehicle loads with selected return periods are computed for all study counting points. The outcome is a mapping methodology with a linked database. The traffic volumes and extreme loads can then be easily found and compared with other highways in the network. This work shows that hazard maps can be implemented to provide importantly and summarized information to understand the risks of extreme traffic loads and to help in the reliability assessment and maintenance strategies of pavements and bridges.

Predictive aircraft maintenance is a complex process, which requires the modeling of the stochastic degradation of aircraft systems, as well as the dynamic interactions between the stakeholders involved. In this paper, we show that the stochastically and dynamically colored Petri nets (SDCPNs) are able to formalize the predictive aircraft maintenance process. We model the aircraft maintenance stakeholders and their interactions using local SDCPNs. The degradation of the aircraft systems is also modeled using local SDCPNs where tokens change their colors according to a stochastic process. These SDCPN models are integrated into a unifying SDCPN model of the entire aircraft maintenance process. We illustrate our approach for the maintenance of multi-component systems with k-out-of-n redundancy. Using SDCPNs and Monte Carlo simulation, we analyze the number of maintenance tasks and potential degradation incidents that the system is expected to undergo when using a remaining useful life(RUL)-based predictive maintenance strategy. We compare the performance of this predictive maintenance strategy against other maintenance strategies that rely on fixed-interval inspection tasks to schedule component replacements. The results show that by conducting RUL-based predictive maintenance, the number of unscheduled maintenance tasks and degradation incidents is significantly reduced.

A submerged floating tunnel (SFT) is a novel structure that allows crossing waterways where immersed tunnels or bridges are not viable. However, no SFT has been built yet mainly, due to lack of experience. In consequence, there are several uncertainties regarding its design and construction. An effect that should be further investigated is the structural response of the SFT under the simultaneous action of waves and currents. For this purpose, extreme values of waves and currents that were generated through a vine-copula model are used as input in a statistical model based on Bayesian Networks (BNs). The BNs are used to study the conditional correlation (i.e the correlation between random variables conditionalized on a given event) between the hydrodynamic forces acting on the SFT and metocean variables such as waves and currents. This methodology was applied to a case study in China for a SFT aimed to be built at the Qiongzhou Strait. Moreover, the BN model was used to test twelve different configurations of the SFT, with varying submergence depths and diameter sizes. The proposed methodology can be used to provide a more realistic estimation of the forces on the SFT by considering the dependence between the variables of interest. Moreover, this methodology can be extended to test different configurations of the SFT and other hydraulic or maritime structures subjected to simultaneous loading.

During the last two decades, increasing vessel size in major German estuaries has led to the significant change of the local loading regime i.e. increased importance of ship-induced waves and currents. As a consequence, the intensity of ship-induced loads has increased considerably, resulting in damage to rock structures such as revetments, training walls, and groynes. Research into the causes of rock structure deterioration by the Federal Waterways Engineering and Research Institute (BAW) has shown that for large ships in relatively narrow waterways, the long-period primary ship wave loading has become the most prescient factor for rock structure damage. Looking into the future, it can be expected that the increase in the vessel dimensions will lead to an increase in the ship-wave loading. For this reason, analysing long-term changing trends of long-period ship waves and vessel speed to understand the wave-structure interaction is of significant importance. In this study, the stochastic characterization of long-period primary wave height, drawdown, and speed of the vessel through the water at Juelssand in the Lower Elbe Estuary was analysed via extreme value analysis and copula modeling, and the bivariate return periods were calculated. The one-parameter bivariate copula was utilized to analyse the data. The dependence pattern between the variables was investigated using five parametric copula families: Gaussian, Gumbel, Clayton, Frank, and student's t.

In reliability engineering, we need to understand system dependencies, cause-effect relations, identify critical components, and analyze how they trigger failures. Three prominent graph models commonly used for these purposes are fault trees (FTs), decision trees (DTs), and binary decision diagrams (BDDs). These models are popular because they are easy to interpret, serve as a communication tool between stakeholders of various backgrounds, and support decision-making processes. Moreover, these models help to understand real-world problems by computing reliability metrics, minimum cut sets, logic rules, and displaying dependencies. Nevertheless, it is unclear how these graph models compare. Thus, the goal of this paper is to understand the similarities and differences through a systematic comparison based on their (i) purpose and application, (ii) structural representation, (iii) analysis methods, (iv) construction, and (v) benefits & limitations. Furthermore, we use a running example based on a Container Seal Design to showcase the models in practice. Our results show that, given that FTs, DTs, and BDDs have different purposes and application domains, they adopt different structural representations and analysis methodologies that entail a variety of benefits and limitations, the latter can be addressed via conversion methods or extensions. Specific remarks are that BDDs can be considered as a compact representation of binary DTs, since the former allows sub-node sharing, which makes BDDs more efficient at representing logical rules than binary DTs. It is possible to obtain cut sets from BDDs and DTs and construct a FT using the (con/dis)junctive normal form, although this may result in a sub-optimal FT structure.

International audience; The identification of the main damage mechanisms and the stress levels is one of the key points for the development of reliability models in the context of accelerated tests. The proposed method is an extension of existing methods allowing an intermediate calculation of an intermediate occurrenceprobability for each failure by estimating the effects of system integration and thus the mutual dependencies of each of the components.; L'identification des principaux mécanismes d'endommagement et des niveaux de sollicitations est l'un des points clés pour l'élaboration des modèles de fiabilité dans un contexte d'essais accélérés. La méthode proposée est une extension de méthodes existantes permettant un calcul intermédiaire de la probabilité d'occurrence pour chaque défaillance en estimant les effets d'intégration système et ainsi les dépendances mutuelles de chacun des composants.

International audience