Dynamic Health Monitoring of Aero-Engine Gas-Path System Based on SFA-GMM-BID

Abstract

This paper proposes a dynamic health monitoring method for aero-engines by extracting more hidden information from the raw values of gas-path parameters based on slow feature analysis (SFA) and the Gaussian mixture model (GMM) to improve the capability of detecting gas-path faults of aero-engines. First, an SFA algorithm is used to process the raw values of gas-path parameters, extracting the effective features reflecting the slow variation of the gas-path state. Then, a GMM is established based on the slow features of the target aero-engine in a normal state to measure its health status. Moreover, an indicator based on the Bayesian inference distance (BID) is constructed to quantitatively characterize the performance degradation degree of the target aero-engine. Considering that the fixed threshold does not suit the time-varying characteristics of the gas-path state, a dynamic threshold based on the maximum information coefficient is designed for aero-engine health monitoring. The proposed method is verified using a set of actual operation data of a certain aero-engine. The results show that the proposed method can better reflect the degradation process of the aero-engine and identify aero-engine anomalies earlier than other aero-engine fault detection methods. In addition, the dynamic threshold can reduce the occurrence of false alarms. All these advantages give the proposed method high value in real-world applications.

Research of the crankshaft high cycle bending fatigue experiment design method based on the modified unscented Kalman filtering algorithm and the SAFL approach

“…As introduced in the above chapter, the acceleration of the loading stage is achieved based on the prediction of the failure time node during this stage. At present, the residual life prediction and some related health monitoring research has been carried out based on various kinds of models and methods [28][29][30][31][32], but the object of prediction among these researches are usually the working life or some other parameters, not the fatigue life. In the published related studies, most of the fatigue life predictions are carried out based on the S-N curve of the material.…”