Reliability Optimization of Structural Deformation with Improved Support Vector Regression Model

Abstract

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. 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. en 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. 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.

A Review on Optimal Design of Fluid Machinery Using Machine Learning Techniques

“…They obtained the best impeller geometry by using the response surface method and analyzed the mixed flow design variables and performance variations in the inlet section of the pump impeller to obtain the optimal shape. Zhu et al [82] applied the Artificial Bee Colony (ABC) algorithm to find the optimal parameters in the traditional Support Vector Regression (SVR) model. They established an improved SVR model, the Improved Support Vector Regression Model (ISRM), and used the Multiple Population Genetic Algorithm (MPGA) to solve the optimization model and program of the ISRM method.…”

“…Zhu et al [82] An improved Support Vector Regression Model (ISRM) was developed, and the Multiple Population Genetic Algorithm (MPGA) was used to optimize the ISRM model and its corresponding program.…”

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

“…ese algorithms are used to optimize the performance of machine learning model to achieve a balance between model accuracy and model generalization. e employed metaheuristic approaches include symbiotic organisms search [42], particle swarm optimization [43,44], the forensic-based investigation optimization [45], equilibrium optimization [20], Harris hawks optimization [46], simulated annealing [47], social spider optimization [48,49], gray wolf optimization [38,50], teaching-learningbased algorithm [51], salp swarm algorithm [52,53], artificial bee colony [54], pigeon-inspired optimization [55], cuckoo search optimization [56], imperialist competitive algorithm [57], moth flame optimization [58], and cuckoo search algorithm [59]. ose previous works have demonstrated the effectiveness of metaheuristic algorithms in optimizing machine learning models and solving complex tasks in various application domains.…”

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“…It is noted that the task of searching for those hyperparameters can be considered as a global optimization problem [28,32,[64][65][66][67][68][69][70][71]. Moreover, since C and σ are searched in continuous space, the number of parameter combinations is infinitely large.…”