Zhang Chun-Yi, Wang Ze, Fei Cheng-Wei, Yuan Zhe-Shan, Wei Jing-Shan, Tang Wen-Zhong
School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China.
Department of Aeronautics and Astronautics, Fudan University, Shanghai 200433, China.
Materials (Basel). 2019 Jul 24;12(15):2341. doi: 10.3390/ma12152341.
The effectiveness of a model is the key factor of influencing the reliability-based design optimization (RBDO) of multi-failure turbine blades in the power system. A machine learning-based RBDO approach, called fuzzy multi-SVR learning method, was proposed by absorbing the strengths of fuzzy theory, support vector machine of regression (SVR), and multi-response surface method. The model of fuzzy multi-SVR learning method was established by adopting artificial bee colony algorithm to optimize the parameters of SVR models and considering the fuzziness of constraints based on fuzzy theory, in respect of the basic thought of multi-response surface method. The RBDO model and procedure with fuzzy multi-SVR learning method were then resolved and designed by multi-objective genetic algorithm. Lastly, the fuzzy RBDO of a turbine blade with multi-failure modes was performed regarding the design parameters of rotor speed, temperature, and aerodynamic pressure, and the design objectives of blade stress, strain, and deformation, and the fuzzy constraints of reliability degree and boundary conditions, as well. It is revealed (1) the stress and deformation of turbine blade are reduced by 92.38 MPa and 0.09838 mm, respectively. (2) The comprehensive reliability degree of the blade was improved by 3.45% from 95.4% to 98.85%. (3) It is verified that the fuzzy multi-SVR learning method is workable for the fuzzy RBDO of complex structures just like a multi-failure blade with high modeling precision, as well as high optimization, efficiency, and accuracy. The efforts of this study open a new research way, i.e., machine learning-based RBDO, for the RBDO of multi-failure structures, which expands the application of machine learning methods, and enriches the mechanical reliability design method and theory as well.
模型的有效性是影响电力系统中多失效汽轮机叶片基于可靠性的设计优化（RBDO）的关键因素。通过吸收模糊理论、回归支持向量机（SVR）和多响应面法的优点，提出了一种基于机器学习的RBDO方法，即模糊多SVR学习方法。基于多响应面法的基本思想，采用人工蜂群算法优化SVR模型参数并考虑基于模糊理论的约束模糊性，建立了模糊多SVR学习方法的模型。然后，采用多目标遗传算法求解并设计了基于模糊多SVR学习方法的RBDO模型和流程。最后，针对转子转速、温度和气动压力等设计参数，以及叶片应力、应变和变形等设计目标，以及可靠度和边界条件的模糊约束，对具有多失效模式的汽轮机叶片进行了模糊RBDO分析。结果表明：（1）汽轮机叶片的应力和变形分别降低了92.38MPa和0.09838mm。（2）叶片的综合可靠度从95.4%提高到98.85%，提高了3.45%。（3）验证了模糊多SVR学习方法对于复杂结构的模糊RBDO是可行的，如具有高建模精度、高优化效率和准确性的多失效叶片。本研究的工作为多失效结构的RBDO开辟了一条新的研究途径，即基于机器学习的RBDO，拓展了机器学习方法的应用，丰富了机械可靠性设计方法和理论。
