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Research Papers

Fault Diagnosis of a Sensor Network: A Distributed Filtering Approach

[+] Author and Article Information
Rajamani Doraiswami

Department of Electrical and
Computer Engineering,
University of New Brunswick,
Fredericton, New Brunswick, Canada E3B5A3

Lahouari Cheded

Systems Engineering Department,
King Fahd University of Petroleum & Minerals,
Dhahran 31261, Saudi Arabia

Contributed by the Dynamic Systems Division of ASME for publication in the Journal of Dynamic Systems, Measurement, and Control. Manuscript received March 15, 2012; final manuscript received February 22, 2013; published online May 21, 2013. Assoc. Editor: Nariman Sepehri.

J. Dyn. Sys., Meas., Control 135(5), 051002 (May 21, 2013) (10 pages) Paper No: DS-12-1083; doi: 10.1115/1.4023894 History: Received March 15, 2012; Revised February 22, 2013

This paper proposes a model-based approach to develop a novel fault diagnosis scheme for a sensor network of a cascade, parallel and feedback combination of subsystems. The objective is to detect and isolate a fault in any of the subsystems and measurement sensors which are subject to disturbances and/or measurement noise. Our approach hinges on the use of a bank of Kalman filters (KF) to detect and isolate faults. Each KF is driven by either a pair (a) of consecutive sensor measurements or (b) of a reference input and a measurement. It is shown that the KF residual is a reliable indicator of a fault in subsystems and sensors located in the path between the pair of the KF's input. The simple and efficient procedure proposed here analyzes each of the associated paths and leads to both the detection and isolation of any fault that occurred in the paths analyzed. The scheme is successfully evaluated on several simulated examples and on a physical fluid system exemplified by a benchmarked laboratory-scale two-tank system to detect and isolate faults including sensor, actuator and leakage ones.

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Figures

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Fig. 1

A sensor network formed of interconnections of subsystems {Gi} and sensors {ksi}

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Fig. 2

A typical closed-loop feedback sensor network

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Fig. 3

Sensor network example

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Fig. 4

Two-tank fluid control system

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Fig. 5

Plots of height, flow rate, control input

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