dc.description.abstract | To improve the current inverter motor development process, the new motor parameter design mode has always relied on the developer’s personal design experience. As a result, the same motor may have different parameter combinations due to the different ideas of the developer, and constantly fine-tuning the motor design parameters to achieve the optimal design makes the combination of design parameters and test verification in the motor development phase quite complicated, which leads to repeated delays in the development schedule.
This research hopes to subvert the current design model through the data mining method of machine learning and establish a model of motor parameter design evaluation. The parameter data collected during the development stage of R company′s motor products are used as the research data, selection and parameters Design-related important features are used for model training and testing. Four regression models such as random forest, Multi-output, Gradient boosting, and Multi-layer perceptron are used to predict, and then the regression model is analyzed through Grid-search and Cross-validation algorithms. Carry out automatic parameter adjustment and select the model through the accuracy of the model and the regression evaluation index, then find the regression model suitable for this research, use the full factorial experimental design model and Euclidean distance measurement algorithm to predict, to find the motor design parameter that is closest to the target value after the target value is given. To shorten the motor design verification time and provide the direction of reference for the motor design.
The regression evaluation index results show that the accuracy of the prediction model presented by the Grid-search (random forest regression) model is more than 91%, the overall explanatory power is more than 91%, and the adjusted model explanatory power is 90%. Therefore, in this study, a random forest regression model with automatic parameter adjustment was selected for parameter selection. Through the experimental design model and the prediction using the Euclidean distance measurement algorithm, the number of motor design verifications can be reduced on average by 2 to 4times, and the conversion can save about 40% of the engineering days.
In addition, for those who are not familiar with the setting of the automatic tuning random forest regression model, it is easier to set the user setting. The evaluation of the predictive model can be used to help developers increase the development speed, shorten the motor design and verification test timeline, to achieve customer expectations. | en_US |