Efficiency Design of Traction Inverters Based on Deep Learning and TRIZ

Abstract

In the design of traction inverter system, there are complex and multiple technical contradictions among various subsystems. Traditional innovative method relies on manual table look-up, which is difficult to effectively deal with multiple technical issues. Due to the above technical difficult, this paper introduces deep learning algorithm into the theory of invention problem solving (Teoriya Resheniya Izobreatatelskikh Zadatch, TRIZ) , concentrates on the collaborative innovation method based on deep learning and TRIZ principles. Through the construction of TRIZ-CRNN model, innovation mechanism contained in innovation cases is explored to realize the collaborative innovation of traction inverter. The TRIZ-CRNN model achieves 99.5% recognition accuracy on the TRIZ innovation case data set, which verifies the feasibility of the collaborative innovation method combining deep learning and TRIZ to be used in innovation mechanism modeling, and provides effective solution and theoretical support for traction inverter technology innovation. In addition, this paper designs human-computer interaction software based on TRIZ-CRNN to improve the operation intelligence of computer aided innovation system and optimize the application feasibility of TRIZ-CRNN algorithm.

Keywords

• triz innovation
• traction inverter
• deep learning
• collaborative innovation
• crnn(convolutional recurrent neural network)