Fault detection via output-based barrier functions
Luca Ballotta (TU Delft - Team Riccardo Ferrari)
Andrea Peruffo (TU Delft - Team Manuel Mazo Jr)
Riccardo M.G. Ferrari (TU Delft - Team Riccardo Ferrari)
Manuel Mazo (TU Delft - Team Manuel Mazo Jr)
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Abstract
Model-based fault detection identifies anomalies by comparing a system's output with the prediction from a model. Although such a technique can be very powerful, it may suffer from the computational complexity of its underlying models, especially for large systems. An alternative approach that circumvents this cost increase uses barrier functions, which abstract the system's behaviour into a single value. In this paper, we propose a fault detection mechanism via output-based barrier functions, that does not require to estimate the full state, copes with noisy processes, and is tailored to safety-critical faults as given by a user-defined safe region. We leverage such a mechanism by introducing so-called p-fault tolerant sets, which guarantee that a faulty system requires at least p time steps before reaching any unsafe state. Our approach is validated through numerical experiments on two systems with linear and nonlinear dynamics, along with the classic three-tank model.
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