In order to improve the reliability of a planet carrier, a simulation method based on multi-objective design optimization was developed in this paper. The objective of the method was to reduce the stress concentration, the deformation, and the quality of the planet carrier by optimizing the structure dimension. A parametric finite element model, which enables a good understanding of how the parameters affect the reliability of planet carrier, was established and simulated by ANSYS-WORKBENCH. The efficiency of the design optimization was improved by using a polynomials response surface to approximate the results of finite element analysis and a screening algorithm to determine the direction of optimization. Furthermore, the multi-objective optimization was capable of finding the global minimum results in the use of the minimum principle on the response surface. Computer simulation was carried out to verify the validity of the presented optimization method, by which the quality and the stability of the planet carrier were significantly reduced and improved, respectively. The methodology described in this paper can be effectively used to improve the reliability of planet carrier.

Energy consuming condition of thermal power unit is reflected by its historical operation data, but the amount of data and the number of energy consumption indices are large. And there are complex and strong correlations among them. These make it difficult to assess the energy consuming condition of unit. This paper proposes a model and method based on improved Principal Component Analysis(PCA) assessing the energy consuming condition of unit. Those assessment problems are solved well by the application of feature extraction and dimensions reducing functions of improved PCA. Meanwhile, traditional assessments’ drawbacks-subjectivity and uncertainty are avoided because the weights of components are determined completely according to the data. Finally, the feasibility and effectiveness of the proposed model are demonstrated with a case study involving energy consumption condition analysis of a 600MW unit in China.