431009 Built-in-Tests for Thermal Fluid Systems of Aerospace Applications

Thursday, November 12, 2015: 1:46 PM
Salon G (Salt Lake Marriott Downtown at City Creek)
William T. Hale, Kyle A. Palmer and George M. Bollas, Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT

The reliability, cost, safety and environmental impact of complex and uncertain engineering systems that are prone to faults, drives fault detection and isolation (FDI) methodologies to the forefront. More specifically, the efficiency and accuracy of these FDI methods are increasingly important among competitive industries, such as aerospace, where system downtime can have a significant impact on profit. Advancement of FDI methods can increase system reliability, availability, and safety through more accurate fault diagnosis. Model-based FDI methods entail advantages over hardware redundant or data-driven approaches in their absence of need for additional hardware, as well as the increased accuracy over wide operating regions, due to their use of first principles and/or empirical correlations.1,2 Recent advances in model-based FDI methods are applicable to nonlinear systems of thermal fluid dynamic processes, such as aircraft environmental control systems (ECS). For instance, looking at the example of an aircraft ECS, significant fouling can occur over time within its primary heat exchanger that can lead to performance degradation and abrupt downstream faults. Current monitoring techniques, like Kalman filtering or observer-based methods, which utilize statistical inferences, such as cumulative sum tests, are incapable of accurately detecting the incipient phenomena of fouling over short periods of time due to the small deviations in the system performance during that time span.3 As a result, the aerospace industry typically uses a shotgun approach in which the heat exchanger is removed for maintenance at arbitrarily chosen time intervals.

In this presentation we propose a method for initiated built-in-tests (iBIT) for aircraft ECS heat exchanger fouling detection, in which the extractable test information is maximized on the basis of a system model. Heat exchanger fouling detection, in terms of quantification of its severity, is critical for aircraft maintenance scheduling and safe operation. We focus on methods for offline fouling detection during aircraft ground handling, where the allowable variability range of admissible inputs is wider. We explore methods of optimal experimental design to estimate heat exchanger inputs and input trajectories that maximize the identifiability of fouling. Fouling metrics, such as thermal fouling resistance, are treated as parameters and analyzed along with a combination of uncertain system inputs, such as inlet temperatures, moisture content, and mass flows. The iBIT design vector, i.e. the system admissible inputs at which fouling is to be estimated, is manipulated to optimize experimental information and expressed via the Fisher Information matrix. Nominal and optimal iBITs are compared with respect to their capability to identify fouling, through parameter estimation, using the corresponding heat exchanger measurements. It is shown that the proposed iBIT methodology for heat exchanger fouling identification allows for accurate and precise estimation of the heat exchanger fouling, when this would have been infeasible with current conventional methods and without the addition of extra measurement devices or other BIT equipment. We close by generalizing the iBIT formulation for continuous non-linear systems with uncertainty.

Acknowledgment

This work was sponsored by the UTC Institute for Advanced Systems Engineering (UTC-IASE) of the University of Connecticut and the United Technologies Corporation. Any opinions expressed herein are those of the authors and do not represent those of the sponsor. Help and guidance by Modelon and Modelon-AB are gratefully acknowledged.

References

1. Hwang, I., Kim, S., Kim, Y. & Seah, C. E. A survey of fault detection, isolation, and reconfiguration methods. IEEE Trans. Control Syst. Technol. 18, 636653 (2010).

2. Simani, S. Model-based fault diagnosis in dynamic systems using identification techniques. (Universita degli Studi di MODENA e REGGIO EMILIA, 2000). doi:10.1007/978-1-4471-3829-7

3. Isermann, R. Model-based fault-detection and diagnosis - Status and applications. Annu. Rev. Control 29, 7185 (2005).


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