Solutions of Feature and Hyperparameter Model Selection in the Intelligent Manufacturing

Abstract

In the era of Industry 4.0, numerous AI technologies have been widely applied. However, implementation of the AI technology requires observation, analysis, and pre-processing of the obtained data, which takes up 60–90% of total time after data collection. Next, sensors and features are selected. Finally, the AI algorithms are used for clustering or classification. Despite the completion of data pre-processing, the subsequent feature selection and hyperparameter tuning in the AI model affect the sensitivity, accuracy, and robustness of the system. In this study, two novel approaches of sensor and feature selecting system, and hyperparameter tuning mechanisms are proposed. In the sensor and feature selecting system, the Shapley Additive ExPlanations model is used to calculate the contribution of individual features or sensors and to make the black-box AI model transparent, whereas, in the hyperparameter tuning mechanism, Hyperopt is used for tuning to improve model performance. Implementation of these two new systems is expected to reduce the problems in the processes of selection of the most sensitive features in the pre-processing stage, and tuning of hyperparameters, which are the most frequently occurring problems. Meanwhile, these methods are also applicable to the field of tool wear monitoring systems in intelligent manufacturing.