François Pérès

This article explores reliability assessment from a systemic and hierarchical perspective. It highlights that while failures occur at the component level, they are often driven by stresses resulting from system use and environmental conditions. To address this, the U-Cycle approach is introduced, providing a structured method to link system decomposition with potential failure mechanisms. The proposed methodology, supported by five complementary views, captures both the organization of the system and its behaviour under operational stress. It integrates a top-down functional analysis, which translates mission profiles into stress variables acting on components, with a bottom-up analysis that examines how failures propagate through subsystems to affect the system-level functionality. Together, these views provide a comprehensive framework for assessing system reliability. A case study on the electrical flight-control function of an aircraft demonstrates the practical implementation of the U-Cycle approach. It shows how stress propagation and degradation mechanisms influence the function’s availability, thereby illustrating the method’s applicability for both predictive reliability modelling during design and for diagnostic reasoning during operation.

The article introduces multidimensional Bayesian networks (MBNs), an advanced extension of traditional Bayesian networks (BNs) and object-oriented BNs (OOBNs). OOBNs effectively modularize complex systems into objects and classes but face limitations when handling heterogeneous components and performing inference in multidimensional spaces. MBNs address these issues by assigning multiple indices to variables, enabling the modeling of large systems across various dimensions, such as space and time. The new algorithms and inference methods allow for more efficient handling of complex relationships in multidimensional systems, making MBNs a powerful tool for decision-making in fields requiring large-scale, dynamic modeling.

The work reported in this article addresses the issue of reliability assessment from a systemic perspective. The general idea of the paper is based on the observation that failures occur at the level of elementary components but are induced by stresses emanating from the form of use of the system and the environment in which they are placed. Within this scope, a so-called U-Cycle approach is developed. It allows, in a matrix framework, to match the levels of decomposition of a system from a functional viewpoint and the resulting failures from a dysfunctional perspective. The modelling of this U-Cycle thus involves taking into account the repercussions of the systems' mission profiles in order to convert them into levels of stress on the basic components in a functional top-down analysis. Then, in a dysfunctional bottom-up study, it leads to characterise the impact of the failure of these components on the operation of the sub-systems to which they belong. The paper describes the principles of methodological instrumentation of this U-cycle based on the superposition of four descriptive views representing the structural, temporal, physical and logical forms of organisation. A case study based on the qualitative modelling of the reliability of an aircraft electrical flight control function is proposed to illustrate the developments.

The article introduces Multidimensional Bayesian Networks (MBNs), an advanced extension of traditional Bayesian Networks (BNs) and Object-Oriented Bayesian Networks (OOBNs). While OOBNs are useful for modelling complex systems by modularizing subsystems into objects and classes, they struggle with heterogeneous components and inference computation in multi-dimensional spaces. MBNs address these issues by assigning multiple indices to variables, enabling the modelling of large systems across various dimensions, such as space and time. The article provides an overview of OOBNs, highlighting their advantages, such as modularity, reusability, and scalability. However, it also discusses limitations, including computational complexity and the challenges of hierarchical modelling and inference, particularly for large-scale systems. The proposed MBN model overcomes these challenges by expanding dimensionality, with key innovations like "virtual nodes" and a structured inference process that allows local computations within objects before propagating information across the system. The new algorithms and inference methods introduced allow for more efficient handling of complex relationships in multidimensional systems, making MBNs a powerful tool for decision-making in fields requiring large-scale, dynamic modelling.

Uncertainty analysis of hydrological models often requires a large number of model runs, which can be time consuming and computationally intensive. In order to reduce the number of runs required for uncertainty prediction, Bayesian networks (BNs) are used to graphically represent conditional probability dependence between the set of variables characterizing a flood event. Bayesian networks (BNs) are relevant due to their capacity to handle uncertainty, combine statistical data and expertise and introduce evidences in real‐time flood forecasting. In the present study, a runoff–runoff model is considered. The discharge at a gauging station located is estimated at the outlet of a basin catchment based on discharge measurements at the gauging stations upstream. The BN model shows good performances in estimating the discharges at the basin outlet. Another application of the BN model is to be used as a reverse method. Knowing discharges values at the outlet of the basin, we can propagate back these values through the model to estimate discharges at upstream stations. This turns out to be a practical method to fill the missing data in streamflow records which are critical to the sustainable management of water and the development of hydrological models.

Intégration de la sûreté de fonctionnement en phase de réponse à appel d’offres Dependability integration from the early bid process