Research On Methods Of Performance Synthetic Estimation And CBM Decision-making Models For Aero-engine

Aero-engine, a valuable and complex systems in aviation industry, is the heart of an aircraft. In order to ensure the better reliability, safety and economy of aero-engine, condition-based maintenance (CBM) policy is populated for airlines in the world. However, for the complexity among the modules or subsystems in the engine, the uncertainty of operation monitoring information, and the fuzziness of maintenance threshold, there are some problems needed to be solved between the engine operation management and maintenance decision-making. Based on this engineering background, combining the key fund project"Study on predicting performance and optimalizing maintenance cost of aeroengine"of civil aviation administration, the thesis deeply discussed the key problem of synthetic performance estimation, modeling of stochastic failure, decision-making of engine removal and forecasting of maintenance levels, which were appeared in the engine stochastic deterioration process, based on the theory of multi-parameters modeling. And these models and methods were proved to be very effective, reasonable, and valuable by some practical examples in airlines. The main contents of this dissertation are as follow:(1) According to the various shortages of previous single-parameter assessment method for engines, the feasibility of applying fuzzy information entropy weights method, which combines rough theory and fuzzy math theory, was studied in the thesis, based on the subjectively weighted method and multi-status parameters. In the example, after comparing with the traditional ways of single-parameter assessment and principle component analysis, fuzzy information entropy weights method showed satisfied result, which needed no prior knowledge with less lose of objective information. After estimating the engine performance synthetically, people can accurately know the health of the whole engine fleet. And this method, which extended the traditional synthetic estimation theory, has been initially applied in airlines, and is proved to be very effective by some engineering examples.(2) Performance deterioration is an important reason of engine removals. But since the randomness of performance deterioration increases the difficulty of maintenance decision-making, it's significative to study the modeling of maintenance decision-making considering engine stochastic failure process. According to the difference between classical hypothesis and practice conditions in modeling of CBM decision-making, the engine times on wing (TOW) due to performance deterioration were looked as life data creatively. And the coherent censoring theory, the correlation of covariats, and basic screening principle were analysed. Furthermore, the characteristics of proportional intensity model (PIM) were studied, which was populated in survival data analysis. Based on thought of PIM, a generalized PIM was put forward, which considered the affects of condition monitoring parameters, maintenance history, etc, and could be applied in complex systems. The resolve process of parameters estimation in this model was detailedly deduced too.(3) The key part of CBM policy during commercial aero-engine operation is engine removal decision-making, whose optimization object is to minimize the maintenance cost under some reliability requirements. Firstly, in the case of fleet's history failure data of CF6 type engine, the generalized PIM was established, which studied the affects of covariates such as condition parameters, maintenance effect, and so on. Then the maintenance threshold was optimized under the object function of long-term average maintenance cost, and the engine removal decision-making control curve was created, which could be an accurate factor of engine maintenance decision-making in airlines. This modeling theory can be generalized to the maintenance decision-making of other type engines, and has important practising value.(4) When the engine on wing is deteriorating greatly and need to remove immediately, the estimation of maintenance levels is very important for airlines. Based on the engine condition monitoring information, a method about variable precision rough Set (VPRS), was introduced to mine the relevance and decision-making rules between maintenance levels and condition parameters. According to the problem of discretization for continuous condition attributes, a discretization method combining self-organization maps(SOM)neural network algorithm and incompatibility estimation was designed. And the certification of some examples showed that this method had obvious dominance, could control the maintenance cost effectively, and had good value for practice.