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Research Papers

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, MA 01854
e-mail: Elaheh_Noursadeghi@student.uml.edu

Ioannis A. Raptis

Autonomous Robotic System Laboratory,
Department of Mechanical Engineering,
University of Massachusetts Lowell,
Lowell, MA 01854
e-mail: Ioannis_Raptis@uml.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received February 27, 2017; final manuscript received August 24, 2017; published online December 19, 2017. Assoc. Editor: Jongeun Choi.

J. Dyn. Sys., Meas., Control 140(5), 051009 (Dec 19, 2017) (12 pages) Paper No: DS-17-1129; doi: 10.1115/1.4037839 History: Received February 27, 2017; Revised August 24, 2017

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.

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Figures

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Fig. 1

Block diagram of the bootstrap PF

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Fig. 4

Schematic of the reduced-order DPFFD algorithm. The thunderbolt markers represent the location of the potential fault modes in the system.

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Fig. 2

Characteristic types of system decomposition: (a) low complexity, many interconnections, (b) balanced complexity/communications with no shared states, and (c) balanced complexity/communications, with shared states. This figure is based on a similar one in Ref. [37].

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Fig. 6

Schematic of the nine-tank system that has been decomposed into two subsystems. The subsystems are specified by the dashed lines while the fault mode locations are noted by double arrows.

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Fig. 3

System digraph of the running example with three states. The circles represent the states, and the squares represent the noise input and scalar variables vectors (fault occurrence terms). The thunderbolt marks illustrate the location of the potential failure modes.

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Fig. 5

Block diagram of the ith DN

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Fig. 7

(a) Structural graph and subsystem decomposition and (b) separation of shared and unshared states for observation fusion. The presence of the β term and the noise V are omitted for illustrative purposes.

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Fig. 10

First case study: probabilities of failure (dashed dotted line) for each fault mode as generated by the two DNs. The solid horizontal line marks the detection threshold. The solid vertical line indicates the occurrence instant of the fault. The states X4, X5, and X6 are shared between the two DNs.

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Fig. 11

Second case study: probabilities of failure (first row), actual (second row), and estimated (third row) values of the tanks water levels at different time instances. Each pixel represents the water level of a tank in the lattice.

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Fig. 12

Second case study: probabilities of failure (dashed line) with respect to time of the DNs with a leak occurring to their respective tank. The solid horizontal line marks the detection threshold. The solid vertical line signals the fault occurrence instant.

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Fig. 8

Spatial distribution of the binary particles during the execution of the failure sensitive filter. The cross markers denote the locations of e1 and e2, respectively.

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Fig. 9

Spatial distribution of the binary particles with different range of i.i.d driving noise when the process is in healthy operating condition. The cross markers denote the locations of e1 and e2.

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