Least Squares Support Vector Machine Based Real-Time Fault Diagnosis Model for Gas Path Parameters of Aero Engines

Least squares support vector machine （LS-SVM） is applied in gas path fault diagnosis for aero engines. Firstly, the deviation data of engine cruise are analyzed. Then, model selection is conducted using pattern search method. Finally, by decoding aircraft communication addressing and reporting system （ACARS） report, a real-time cruise data set is acquired, and the diagnosis model is adopted to process data. In contrast to the radial basis function （RBF） neutral network, LS-SVM is more suitable for real-time diagnosis of gas turbine engine.

Extrapolation for Aeroengine Gas Path Faults with SVM Bases on Genetic Algorithm

Mining aeroengine operational data and developing fault diagnosis models for aeroengines are to avoid running aeroengines under undesired conditions.Because of the complexity of working environment and faults of aeroengines,it is unavoidable that the monitored parameters vary widely and possess larger noise levels.This paper reports the extrapolation of a diagnosis model for 20 gas path faults of a double-spool turbofan civil aeroengine.By applying support vector machine(SVM)algorithm together with genetic algorithm(GA),the fault diagnosis model is obtained from the training set that was based on the deviations of the monitored parameters superimposed with the noise level of 10%.The SVM model(C=24.7034;γ=179.835)was extrapolated for the samples whose noise levels were larger than 10%.The accuracies of extrapolation for samples with the noise levels of 20%and 30%are 97%and 94%,respectively.Compared with the models reported on the same faults,the extrapolation results of the GASVM model are accurate.

Knowledge and data jointly driven aeroengine gas path performance assessment method

Aeroengines,as the sole power source for aircraft,play a vital role in ensuring flight safety.The gas path,which represents the fundamental pathway for airflow within an aeroengine,directly impacts the aeroengine's performance,fuel efficiency,and safety.Therefore,timely and accurate evaluation of gas path performance is of paramount importance.This paper proposes a knowledge and data jointly driven aeroengine gas path performance assessment method,combining Fingerprint and gas path parameter deviation values.Firstly,Fingerprint is used to correct gas path parameter deviation values,eliminating parameter shifts caused by non-component performance degradation.Secondly,coarse errors are removed using the Romanovsky criterion for short-term data divided by an equal-length overlapping sliding window.Thirdly,an Ensemble Empirical Mode Decomposition and Non-Local Means(EEMD-NLM)filtering method is designed to“clean”data noise,completing the preprocessing for gas path parameter deviation values.Afterward,based on the characteristics of gas path parameter deviation values,a Dynamic Temporary Blended Network(DTBN)model is built to extract its temporal features,cascaded with Multi-Layer Perceptron(MLP),and combined with Fingerprint to construct a Dynamic Temporary Blended AutoEncoder(DTB-AutoEncoder).Eventually,by training this improved autoencoder,the aeroengine gas path multi-component performance assessment model is formed,which can sufficiently decouple the nonlinear mapping relationship between aeroengine gas path multi-component performance degradation and gas path parameter deviation values,thereby achieving the performance assessment of engine gas path components.Through practical application cases,the effectiveness of this model in assessing the aeroengine gas path multi-component performance is verified.

Investigations into sensing characteristics of circular thin-plate electrostatic sensors for gas path monitoring

Circular thin-plate electrostatic sensors are promising in gas path monitoring due to their advantages of non-intrusiveness and easy installation. The spatial sensitivity and filtering effect are two important performance parameters. In this paper, an analytically mathematical model of induced charge on a circular thin-plate electrode is first derived. Then the spatial sensitivity and filtering effect of the circular electrostatic sensor are investigated by numerical calculations. Finally,experimental studies are performed to testify the theoretical results. Both theoretical and experimental results demonstrate that circular thin-plate electrostatic sensors act as a low-pass filter in the spatial frequency domain, and both the spatial filtering effect and the temporal frequency response characteristics depend strongly on the spatial position and velocity of the charged particle. These conclusions can provide guidelines for the optimal design of circular thin-plate electrostatic sensors.

基于模型的航空发动机气路故障容错控制方法

A fault tolerant control method is proposed in this paper for a turbofan engine gas path degradation through the full flight envelope. A Quantum-behaved Particle Swarm Optimization(QPSO) algorithm is applied to obtain engine inputs adjustments, which contribute to construct off-line performance accommodation interpolation schedules. With a double closed-loop control system structure, command control is corrected based on real-time fault diagnostic results. Simulations indicate that fault tolerant control could reduce thrust and stall margin loss effectively in gas path faults.