A Comprehensive Prognostics Approach For Prediction Aero-engine Bearing Life

Development of practical and verifiable prognostic approaches for gas turbine engine bearings will play a critical role in improving the reliability and safety of aircraft engines. In present study Grade-life was used to describe the bearing's service life, which means the entire service life was divided into four stages: good bearing condition, initial defect condition, damaged bearing condition and failure coming condition. In addition, a Grade-life prognostic model was presented that utilized available airborne sensor information to calculate and assessment Grade-life for aeroengine bearings. The model comprised of mathematical model and diagnostic estimate model.The mathematical model was a physics-based modeling.In order to set up the formula for bearing's life calculating, the mechanical models of roller bearings were set up by thequasi-dynamic method, and prognostics equations for bearing's life calculating was modified based on quasi-dynamic method and basic dynamic capacity theory of bearings. The rules of the life in different structure parameters and load parameters were analyzed, then a load spectra compilation method was presented for aero-engine bearings based on statistical analysis of the flight parameter variety character and aero-engine control schedule. Utilizes information from the Sensed Data module to calculate the cumulative damage sustained by the bearing since it was first installed and the Predition Grade-life (PGL) could be captured based on reliability.Diagnostic estimate model was an"empirical"lifetime model, in which"experience"value of Grade-life (EGL) was assessed based on vibration signals intelligently. Signal feature extraction (Grade-life feature vector) and pattern recognition algorithm of Grade-life were keys to construct the model.Vibration signals of the rolling bearing were analyzed, the selection criterion of the Grade-life feature vectors should include the diagnostic ability, the sensitivity, the consistency and the amount of calculation. Finally, two types Grade-life feature vectors were identified :(1) some time-domain statistical parameters were selected to constructe the feature vector; (2) construction of feature vector by extract feature in every frequency bands by the wavelet packet analysis. Due to the stochastic nature of a failure propagation process, the uncertain mechanical failture model of bearings, the modeling methods of Diagnostic estimate model based on BP neural networks and Support Vector Machines (SVM) were presented and developed in detail. Two examples were choosed to establish the model respectively based on BP network. The result showed that BP network based on Bayesian Regularization method could be successfully used for modeling of EGL. For SVM, the SVM model in establishing the identification model by using bearing test stand run-to-failure data as learning samples was employed. A detailed contrast was performed between the two methods, the result showed the SVM had more evident superiority and it was a technique even more suitable for practical application. In addition, the performance of Diagnostic estimate model was influenced not only by pattern recognition algorithm, but alse by the selection of Grade-life feature vector. For example, extract feature in every frequency bands by the wavelet packet analysis was better than the feature vector of time-domain statistical parameters.The final Grade-life of bearings was determined by fusion of PGL and EGL value by fuzzy logic organon, which reduced the mathematical model uncertainty's affection towards prediction results of the mathematical model. The validity and creditability of model had been demonstrated by bearing test stand run-to-failure dates.Experimental equipment was constructed to perform accelerated testing on bearings for method verification, in which load coule be put on test bearings by a hydraulic loading mechanism accurately, and the vibration signals were acquired continuously by a sensors and data acquisition sub-system.The Grade-life model provided a scientific, reliable, and unique methodology prognostic the lifetime of aero-engine bearings.