Blade tip clearance (BTC) measurement and active clearance control (ACC) are becoming crucial technologies in aero-engine health monitoring so as to improve the efficiency and reliability as well as to ensure timely maintenance. Eddy current sensor (ECS) offers an attractive option for BTC measurement due to its robustness, whereas current approaches have not considered two issues sufficiently. One is that BTC affects the response time of a measurement loop, the other is that ECS signal decays with increasing speed. This paper proposes a speed adjustment model (SAM) to deal with these issues in detail. SAM is trained using a nonlinear regression method from a dynamic training data set obtained by an experiment. The Levenberg–Marquardt (LM) algorithm is used to estimate SAM characteristic parameters. The quantitative relationship between the response time of ECS measurement loop and BTC, as well as the output signal and speed are obtained. A BTC measurement method (BTCMM) based on the SAM is proposed and a geometric constraint equation is constructed to assess the accuracy of BTC measurement. Experiment on a real-time BTC measurement during the running process for a micro turbojet engine is conducted to validate the BTCMM. It is desirable and significative to effectively improve BTC measurement accuracy and expand the range of applicable engine speed.

In-cylinder oxygen concentration (ICOC) is critical for advanced combustion control of internal combustion engines, and is hard to be accessed in commercial measurements. In existing research, ICOC is predicted by conventional dynamical model based on mass/energy conservation, which suffers from uncertainties such as inaccuracy of volumetric efficiency or the error of orifice geometry. In this paper, we enhance the ICOC estimation by implementing two vital strategies. Firstly, we introduce a method called virtual measurement to resist the conventional model uncertainties, in this method we modeling the ICOC as a function of ignition delay which can be obtained by measuring the in-cylinder pressure. Secondly, we apply Kalman filter to fuse the ICOC results from the conventional dynamical model and the virtual measurement. The data fusion algorithm turns the estimation to a predictor-corrector fashion, which further improves the overall accuracy and robustness. The proposed approach is validated through a calibrated GT-Power engine model. The results show that the estimation error can be achieved form at worst 0.03 to at best 0.01 on steady state.