Reduced-Order Distributed Fault Diagnosis for Large-Scale Nonlinear Stochastic Systems

Probabilistic Fault Diagnosis for Nonlinear Stochastic Systems

The paper focuses on fault detection and isolation (FDI) in stochastic nonlinear systems using Bayesian statistics. The main concept of this approach leans on a probabilistic model, which estimates the occurrences of faults by probabilities. The methodology consists in probabilistic mapping of the measured data into a fault variable, which acts as an indicator of considered modes of the systems...

A Particle-Filtering Based Approach for Distributed Fault Diagnosis of Large-Scale Interconnected Nonlinear Systems

This paper deals with the problem of designing a distributed fault detection and isolation algorithm for nonlinear large-scale systems that are subjected to multiple fault modes. To solve this problem, a network of detection nodes is deployed to monitor the monolithic system. Each node consists of an estimator with partial observation of the system’s state. The local estimator executes a distri...

Reduced order motion planning for nonlinear symmetric distributed robotic systems

This paper develops a motion planning algorithm which exploits symmetry in distributed systems to reduce complexity and motion planning design time. The motion planning computations are carried out on a reduced order system, then extended to larger-order equivalent systems in such a way that the objectives of the larger system are satisfied and collision avoidance is guaranteed. The algorithm m...

Fault Diagnosis for Nonlinear Systems

Comparative numerical analysis using reduced-order modeling strategies for nonlinear large-scale systems

We perform a comparative analysis using three reduced-order strategies – Missing Point Estimation (MPE) method, Gappy POD method, and Discrete Empirical Interpolation Method (DEIM) – applied to a biological model describing the spatio-temporal dynamics of a predatorprey community. The comparative study is focused on the efficiency of the reduced-order approximations and the complexity reduction...