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research-article

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

[+] Author and Article Information
Elaheh Noursadeghi

Autonomous Robotic System Laboratory, Department of Mechanical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts 01854
elaheh_noursadeghi@student.uml.edu

Ioannis Raptis

Autonomous Robotic System Laboratory, Department of Mechanical Engineering, University of Massachusetts Lowell, Lowell, Massachusetts 01854
ioannis_raptis@uml.edu

1Corresponding author.

ASME doi:10.1115/1.4037839 History: Received February 27, 2017; Revised August 24, 2017

Abstract

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.

Copyright (c) 2017 by ASME
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