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

2015 ◽  
Author(s):  
Elaheh Noursadeghi ◽  
Ioannis Raptis
Keyword(s):  
Particle Filtering ◽  
Large Scale ◽  
Stochastic Systems ◽  
Fault Detection And Isolation ◽  
Consensus Algorithm ◽  
Nonlinear Stochastic Systems ◽  
Graph Theoretic ◽  
Progression Model ◽  
Distributed Fault Detection ◽  
Distributed Average Consensus

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 distributed variation of the particle filtering algorithm; that process the local sensor measurements and the fault progression model of the system. In addition, each node communicates with its neighbors by sharing pre-processed information. The communication topology is defined using graph theoretic tools. The information fusion between the neighboring nodes is performed by a distributed average consensus algorithm to ensure the agreement on the value of the local estimates. The simulation results demonstrate the efficiency of the proposed approach.

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

2017 ◽  
Vol 140 (5) ◽  
Author(s):  
Elaheh Noursadeghi ◽  
Ioannis A. Raptis
Keyword(s):  
Large Scale ◽  
Stochastic Systems ◽  
Monitoring Network ◽  
Diagnostic Algorithm ◽  
Fault Detection And Isolation ◽  
Statistical Confidence ◽  
Design Flexibility ◽  
Distributed Fault Detection ◽  
Fault Mode ◽  
Low Order

This paper deals with the distributed fault detection and isolation problem of uncertain, nonlinear large-scale systems. The proposed method targets applications where the computation requirements of a full-order failure-sensitive filter would be prohibitively demanding. The original process is subdivided into low-order interconnected subsystems with, possibly, overlapping states. A network of diagnostic units is deployed to monitor, in a distributed manner, the low-order subsystems. Each diagnostic unit has access to a local and noisy measurement of its assigned subsystem's state, and to processed statistical information from its neighboring nodes. The diagnostic algorithm outputs a filtered estimate of the system's state and a measure of statistical confidence for every fault mode. The layout of the distributed failure-sensitive filter achieves significant overall complexity reduction and design flexibility in both the computational and communication requirements of the monitoring network. Simulation results demonstrate the efficiency of the proposed approach.

scholarly journals pth Mean Practical Stability for Large-Scale Itô Stochastic Systems with Markovian Switching

2012 ◽  
Vol 2012 ◽  
pp. 1-11 ◽  
Author(s):  
Yan Yun ◽  
Huisheng Shu ◽  
Yan Che
Keyword(s):  
Comparison Principle ◽  
Large Scale ◽  
Stochastic Systems ◽  
Markovian Switching ◽  
Practical Stability ◽  
Nonlinear Stochastic Systems ◽  
Deterministic Systems ◽  
The One

Motivated by the study of a class of large-scale stochastic systems with Markovian switching, this correspondence paper is concerned with the practical stability in thepth mean. By investigating Lyapunov-like functions and the basic comparison principle, some criteria are derived for various types of practical stability in thepth mean of nonlinear stochastic systems. The main contribution of these results is to convert the problem of practical stability in thepth mean of stochastic systems into the one of practical stability of the comparative deterministic systems.

scholarly journals Conditional Gaussian Systems for Multiscale Nonlinear Stochastic Systems: Prediction, State Estimation and Uncertainty Quantification

Entropy ◽  
2018 ◽  
Vol 20 (7) ◽  
pp. 509 ◽  
Author(s):  
Nan Chen ◽  
Andrew Majda
Keyword(s):  
Large Scale ◽  
Stochastic Systems ◽  
Planck Equation ◽  
Reaction Diffusion ◽  
Small Scale ◽  
Model Errors ◽  
Nonlinear Stochastic Systems ◽  
Hybrid Strategy ◽  
Highly Nonlinear ◽  
Non Gaussian

A conditional Gaussian framework for understanding and predicting complex multiscale nonlinear stochastic systems is developed. Despite the conditional Gaussianity, such systems are nevertheless highly nonlinear and are able to capture the non-Gaussian features of nature. The special structure of the system allows closed analytical formulae for solving the conditional statistics and is thus computationally efficient. A rich gallery of examples of conditional Gaussian systems are illustrated here, which includes data-driven physics-constrained nonlinear stochastic models, stochastically coupled reaction–diffusion models in neuroscience and ecology, and large-scale dynamical models in turbulence, fluids and geophysical flows. Making use of the conditional Gaussian structure, efficient statistically accurate algorithms involving a novel hybrid strategy for different subspaces, a judicious block decomposition and statistical symmetry are developed for solving the Fokker–Planck equation in large dimensions. The conditional Gaussian framework is also applied to develop extremely cheap multiscale data assimilation schemes, such as the stochastic superparameterization, which use particle filters to capture the non-Gaussian statistics on the large-scale part whose dimension is small whereas the statistics of the small-scale part are conditional Gaussian given the large-scale part. Other topics of the conditional Gaussian systems studied here include designing new parameter estimation schemes and understanding model errors.

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