Performance Monitoring Based on Improved Adaptive Kalman Filtering for Turboshaft Engines Under Network Uncertainties

Aero-engine performance monitoring is a core component of the engine health management system and an important approach to enhancing flight safety and reliability. Meanwhile, to improve engine operation efficiency, control systems are evolving from traditional centralized architectures to distributed control architectures. To alleviate the negative impact of network uncertainties, this paper proposes a Distributed Adaptive Kalman Filter (DAKF), which resolves the estimation performance degradation of the classical Kalman Filter under network uncertainty by designing measurement reconstruction and buffer-based signal fusion strategies, expanding the engineering applicability of the Kalman Filter in distributed control architectures. Furthermore, a distributed hardware architecture was established based on the time-triggered protocol/class (TTP/C) bus protocol, communication programs between simulation nodes were developed, and the proposed DAKF algorithm was deployed in the hardware architecture for experimental validation. This study focuses on the steady-state operations of the turboshaft engine to investigate the performance of the proposed distributed Kalman Filter algorithm under network uncertainties. The results demonstrated the effectiveness of the proposed method, providing a basis for the engineering application of distributed performance monitoring methods.