Abstract
Deformation is one important failure mode of turbine blades. The quality of a model seriously influences the reliability optimization of turbine blades in turbo machines. To improve the reliability optimization of turbine blades, this paper proposes a novel machine learning-based reliability optimization approach, named improved support vector regression (SR) model (ISRM) method, by fusing artificial bee colony (ABC), traditional SR model, and multipopulation genetic algorithm (MPGA). In this proposed method, the ABC algorithm was applied to find the optimal parameters in the SR model to establish accurate ISRM, following the thought of the surrogate model method and the randomness of constraints. Then the reliability optimization model and procedure with the ISRM method were resolved by the MPGA. Regarding many design parameters (i.e., rotor speed, temperature, and aerodynamic pressure), design objective (i.e., blade deformation), and the randomness constraints of reliability degree and boundary conditions, we performed the reliability optimization of a turbine blade deformation. From the optimization results, we find that the turbine blade deformation is reduced by 0.09329 mm, and the ISRM learning method can improve the reliability optimization design of complex structures with the emphasis on modeling precision and optimization efficiency. The works of this paper provide a machine learning-based reliability optimization approach for the reliability optimization of complex structures and enrich and develop mechanical reliability theory and methods.
Highlights
Deformation is one important failure mode of turbine blades. e quality of a model seriously influences the reliability optimization of turbine blades in turbo machines
The artificial bee colony (ABC) algorithm was applied to find the optimal parameters in the support vector regression (SR) model to establish accurate ISRM, following the thought of the surrogate model method and the randomness of constraints. en the reliability optimization model and procedure with the ISRM method were resolved by the multipopulation genetic algorithm (MPGA)
We find that the turbine blade deformation is reduced by 0.09329 mm, and the ISRM learning method can improve the reliability optimization design of complex structures with the emphasis on modeling precision and optimization efficiency. e works of this paper provide a machine learning-based reliability optimization approach for the reliability optimization of complex structures and enrich and develop mechanical reliability theory and methods
Summary
Support vector machine (SVM) is one of the typical machine learning methods. e SVM was first proposed in data mining by regression and classification which are called SVM of regression (SR) and SVM of classification (SC) [28, 29]. e basis of an SVM model is statistical learning algorithm so that the SVM is suitable for small samples of structural design analysis, which are gained from a few FE simulations. e SR model is good at the solution of high nonlinear problems between input variables and output response, by introducing a maximum classification margin subject to inequality constraints [30]. Erefore, this paper considers the effect of design parameters and constraint conditions and develops a machine learning-based reliability optimization approach, that is, improved SR model (ISRM) method, based on the artificial bee colony (ABC) algorithm, traditional SR model, and multipopulation genetic algorithm (MPGA), to accomplish the reliability optimization of turbine blade deformation. En, the optimal individuals of each excellent population are selected via artificial selection operator and are regarded as structure elite population to search for the optimal value of objective function In this case, the MPGA is essentially a combination of multiple GAs in line with a specific relationship. Normalizing input samples and output samples as the training samples is to find the optimal parameters of the SR model by using an artificial bee colony (ABC) algorithm [29] and to build ISRM for the deformation of turbine blades.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
