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Access type: UnknownAuthor: search.filters.author.Élodie Chanthery

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    Near-optimal decentralized diagnosis via structural analysis
    (Institute of Electrical and Electronics Engineers Inc., 2022-12-01)
    Health monitoring of current complex systems significantly impacts the total cost of the system. Centralized fault diagnosis architectures are sometimes prohibitive for large-scale interconnected systems, such as distribution systems, telecommunication networks, water distribution networks, or fluid power systems. Confidentiality constraints are also an issue. This article presents a decentralized fault diagnosis method that only requires the knowledge of local models and limited knowledge of their neighboring subsystems. The method, implemented in the decentralized diagnoser design ($D^{3}$) algorithm, is based on structural analysis and can advantageously be applied to high-dimensional systems, linear or nonlinear. Using the concept of isolation on request, a hierarchy is built according to diagnostic objectives. The resulting diagnoser is based on analytical redundancy relations (ARRs) generated along the hierarchy. Their number is optimized via binary integer linear programming (BILP) while still guaranteeing maximal diagnosability at each level.$D^{3}$proves of lower time complexity than its centralized equivalent. It is successfully applied to a nonlinear combined cycle gas-turbine power plant.
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    Diagnosis test selection for distributed systems under communication and privacy constraints
    (Springer Science+Business Media, 2025-05-01)
    Distribution is often necessary for large-scale systems because it makes monitoring and diagnosis more manageable from both computational and communication costs perspectives. Decomposing the system into subsystems may also be required to satisfy geographic, functional, or privacy constraints. The selection of diagnosis tests guaranteeing some level of diagnosability must adhere to this decomposition by remaining as local as possible in terms of the required sensor variables. This helps minimize communication costs. In practical terms, this means that the number of interconnections between subsystems should be minimized while keeping diagnosability, i.e., fault isolation capability, at its maximum. This paper differentiates itself from existing literature by leveraging flexibility in forming the subsystems. Through structural analysis and graph partitioning, we address the combined challenges of constrained decomposition of a large-scale system into subsystems and the selection of diagnosis tests that achieve maximal diagnosability with minimal subsystem interconnection. The proposed solution is implemented through an iterative algorithm, which is proven to converge. Its efficiency is demonstrated using a case study in the domain of water networks.
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