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Abstract

In the field of fault diagnosis and engine functional health management, onboard sensor analytical redundancy modeling serves as a critical technology. However, the modeling of analytical redundancy models for engine parameters is challenged by hardware constraints and the demand for high precision. This paper proposes a modeling method for Onboard Sensor Analytical Redundancy Model (OSARM) based on flight data, which combines the advantages of high precision, fast computation, and small storage footprint. The steady-state portion is established using the Kullback-Leibler (KL) Divergence Minimization K-Means Algorithm (KLDK-Means), with the baseline model of the engine obtained through a Physics-Based Model (PBM). Considering the impact of inlet operating conditions on the distribution of steady-state data, the accuracy of the steady-state baseline model is further improved by constructing a Modified Baseline Model (MBM). The dynamic modeling portion innovatively introduces the concept of nonlinear levels and employs the Nonlinear Levels Seeker Optimization Algorithm (NL-SOA) to construct a dynamic model, which improves upon traditional dynamic error optimization methods. The proposed method is validated using real flight data. The results show that compared to the Hammerstein model, OSARM achieved a reduction in maximum error of 32.4%. The modeling approach presented in this paper meets the requirements for precision modeling while satisfying the need for fast computation and small storage footprint.

Keywords

turboshaft engine on-board model baseline model analytical redundancy model nonlinear levels

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Analytical redundancy modeling of onboard sensors for engine parameter estimation using flight data

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