Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics
Open accessAbstract
In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated.
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The state covariance matrix estimated from each Kalman filter can be compared for FDI analysis of sensors.
“The state covariance matrix estimated from each filter can be compared for FDI analysis of sensors [52,53].”
From Sensor-Based Prognostic Health Management of Advanced Driver Assistance System for Autonomous Vehicles: A Recent Survey (2022) · cites this paper as [52]
The Sensor Fault Detection section (page unavailable) describes a bank of Kalman filters used for sensor FDI. But it says the fault indicator is a weighted sum of squared sensor-measurement residuals, compared with a detection threshold—not a comparison of the filters’ estimated state covariance matrices. (LLM, conf=0.86)
Experimental — accuracy not yet measured
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- Authors
- Kobayashi, T., & Simon, D. L.
- Affiliations
- QSS Group, Inc., Cleveland, OH
- DEVCOM Army Research Laboratory (US)
- Venue
- Volume 1: Turbo Expo 2003
- Publisher
- ASMEDC
- Published
- 2003-01-01
- Pages
- 461-470
- OpenAlex
- W2151462123
- Cited by
- 198
- References
- 17
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 5 · 2023: 9 · 2022: 13 · 2021: 8 · 2020: 12 · 2019: 9 · 2018: 11 · 2017: 14 · 2016: 12 · 2015: 12 · 2014: 7 · 2013: 13 · 2012: 6
- FWCI
- 8.87
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- NASA Technical Reports Server (NASA) (submittedVersion)
- Defense Technical Information Center (DTIC) (submittedVersion)
- Funders
- National Aeronautics and Space Administration
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Engineering and Test Systems
- Physical Sciences › Engineering › Control and Systems Engineering › Aerospace Engineering and Applications
- Keywords
- Kalman filter bank, fault detection and isolation, aircraft gas turbine engines, sensor faults, actuator faults
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