Research Papers

Fault Detection and Isolation for Complex Thermal Management Systems

[+] Author and Article Information
Pamela J. Tannous

Mechanical Science and Engineering
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: tannous2@illinois.edu

Andrew G. Alleyne

Mechanical Science and Engineering
University of Illinois at Urbana-Champaign,
Urbana, IL 61801
e-mail: alleyne@illinois.edu

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT,AND CONTROL. Manuscript received May 15, 2018; final manuscript received January 26, 2019; published online February 21, 2019. Assoc. Editor: Youngsu Cha.

J. Dyn. Sys., Meas., Control 141(6), 061008 (Feb 21, 2019) (10 pages) Paper No: DS-18-1238; doi: 10.1115/1.4042675 History: Received May 15, 2018; Revised January 26, 2019

This paper presents a fault detection and isolation (FDI) approach for actuator faults of complex thermal management systems. In the case of safety critical systems, early fault diagnosis not only improves system reliability, but can also help prevent complete system failure (i.e., aircraft system). In this work, a robust unknown input observer (UIO)-based actuator FDI approach is applied on an example aircraft fluid thermal management system (FTMS). Robustness is achieved by decoupling the effect of unknown inputs modeled as additive disturbances (i.e., modeling errors, linearization errors, parameter variations, or model order reduction errors) from the residuals generated from a bank of UIOs. Robustness is central to avoid false alarms without reducing residual sensitivity to actual faults in the system. System dynamics are modeled using a graph-based approach. A structure preserving aggregation-based model-order reduction technique is used to reduce the complexity of the dynamic model. A reduced-order linearized state space model is then used in a bank of UIOs to generate a set of structured robust (in the sense of disturbance decoupling) residuals. Simulation and experimental results show successful (i.e., no false alarms) actuator FDI in the presence of unknown inputs.

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Fig. 1

An example graph-based model representing mass flow dynamics of an aircraft fluid thermal management system

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Fig. 2

Testbed representing an example aircraft FTMS

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Fig. 3

Schematic of the testbed shown in Fig. 2

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Fig. 4

Full-order graph-based model of the mass flow dynamics of the FTMS

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Fig. 6

Reduced-order (left) versus full order (right) graph-based models of the mass flow dynamics of the FTMS

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Fig. 7

Pressure dynamics of the graph-based model

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Fig. 8

FDI—scenario 1 (Nonfaulty case)

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Fig. 9

FDI—scenario 2 (faulty case)

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Fig. 10

FDI—scenario 3 (faulty case)



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