Research On Key Techniques Of Aeroengine Wear Fault Intelligent Diagnosis

Aero-engine has extremely complex structure, and easily has broken down all kinds ofmechanical failure working in harsh conditions of high temperature and high speed. According tosome statistics, in the factors that cause various types of flight accident, the proportion of the enginefailure reason is generally in the range of25%to30%. Moreover, the gear and bearing wear failurein the aero-engine rotor system and transmission system is the main fault occurred in the study andapplication. Therefore, it is critical to diagnose and predict the aircraft engine wear fault timely andeffectively in order to elevate the flight safety, lower the engine maintenance cost, implementaero-engine condition based maintenance. However, because of the complexity of the aero-engine,the relationships between the various wear data and the wear failure is fuzzy, nonlinear and uncertainrelationship, and the traditional methods can’t meet the requirements of the wear fault diagnosis. Inview of this, this paper introduces the modern artificial intelligence and pattern recognitiontechnology into the aero-engine wear fault diagnosis and has commenced the study on some pivotalproblems about aero-engine wear fault intelligent diagnosis. Now the summary of main workingcontents in this paper is as follows:(1) The establishment of the wear threshold that is not limited to the normal distributionassumption. The establishment of the wear threshold based on Support Vector Machine is proposedwith abandoning the traditional normal distribution assumption of the sample data. The probabilitydensity is estimated from a large number of oil samples by using Support Vector Machine, and thenthe wear threshold is obtained according to the probability density. This method takes advantage ofserious advantages of Support Vector Machine such as the global optimal solutions, goodgeneralization ability, and the sparse solution. This method is more scientific and reasonable incontrast to the traditional statistical methods. Lastly, the verification analysis is done by using theactual Aero-engine spectroscopic data, and the results suggest the correctness and effectiveness ofthe method.(2) Combinational Forecast Method of the wear trend. It is critical to estimate the futuredevelopment trend through making Mathematical Modeling for oil sample data in order to predictthe aircraft engine wear trend, and forecast and evaluate the development trend of the fault as earlyas possible, so as to prevent major accidents and schedule maintenance work in time. In view of this,this paper proposed the combinational forecast method based on Least Square Support VectorMachine. The first step is using AR model、GM(1,1) model and BP neural network model to predictindividually, and the next step is combination forecast based on LSSVM, at the same timeoptimizing the parameters of SVM method by using particle swarm algorithm. This method solvessome issues such as comprehensive information of the single forecasting model, and sensitive to themodel form setting. Lastly, the verification analysis is done by using the actual Aero-enginespectroscopic data, and the results show that, compared with the individual forecast methods, thecombinational forecast method has greatly improved the prediction precision.(3) Automatic extraction of wear fault diagnosis knowledge rules. In order to solve the defectsof the current aircraft engine wear fault intelligent diagnosis expert system, such as the weakness ofcapability of knowledge acquisition, the difficulty of knowledge updating, poor adaptability of knowledge, and so on. In this paper, a data mining approach based on SVM is proposed to extractwear rules automatically. In this method, the first step is to choose the features of the sample data byusing Genetic Algorithm for improving the comprehensibility of the knowledge rules. Then the SVCalgorithm is adopted to get the Clustering Distribution Matrix of the sample data whose featureshave been chosen. Finally, hyper-rectangle rules are constructed on the base of the ClusteringDistribution Matrix. In order to make the rules more concise, and easier to be explained,hyper-rectangle rules are simplified further by using rules combination, dimension reduction andinterval extension. In addition, the SMOTE algorithm is adopted to resample fault samples in orderto solve the serious imbalance problem of samples. Meanwhile, the interface between the foreignwell-known open source software in data mining called Weka and expert system was researched,and the data mining method in Weka is used to extract knowledge automatically from aircraft enginewearing fault data. Lastly, the verification analysis is done by using the actual Aero-enginespectroscopic data, and the results suggest the correctness and effectiveness of the method.(4) Engine wear fault fusion diagnosis method based on Multi-Agent. This method improvesdiagnostic accuracy by using of characteristics and advantage and comprehensive use of redundancyand complementarity of various oil analysis methods. The Multi-Agent diagnosis System isconstituted by Particle Count Agent, Physicochemical Analysis Agent, Ferrograph Analysis Agent,Spectrometric Analysis Agent, General Control Agent, Scheduling Agent, Communication Agent,Fusion Diagnosis Agent sample data and knowledge rule database, and man-machine intelligentinterface. In this paper, according to the actual situation of aero-engine wear fault diagnosis, eachagent diagnosis rules are given. At last, the test results of the specific oil analysis data show theeffectiveness of the multi-agent fusion diagnosis.(5) Finally, the intelligent methods of this paper researched are applied to the developedaero-engine wear fault diagnosis expert system cooperated with Chengdu aircraft industrial (group)co., LTD and the Institute of Airforce Equipment. The establishment of the wear threshold, the weartrend, fusion diagnosis and automatic extraction of wear fault diagnosis knowledge rules are realized.The application results show that the work researched has greatly increased the intelligent andautomation level of the aero-engine wear fault intelligent diagnosis expert system.