Training Data Selection by Categorical Variables for Better Rare Event Prediction in Multiple Products Production Line

Manufacturers are struggling to use data from multiple products production lines to predict rare events. Improving the quality of training data is a common way to improve the performance of algorithms. However, there is little research about how to select training data quantitatively. In this study, a training data selection method is proposed to improve the performance of deep learning models. The proposed method can represent different time length multivariate time series spilt by categorical variables and measure the (dis)similarities by the distance matrix and clustering method. The contributions are: (1) The proposed method can find the changes to the training data caused by categorical variables in a multivariate time series dataset; (2) according to the proposed method, the multivariate time series data from the production line can be clustered into many small training datasets; and (3) same structure but different parameters prediction models are built instead of one model which is different from the traditional way. In practice, the proposed method is applied in a real multiple products production line dataset and the result shows it can not only significantly improve the performance of the reconstruction model but it can also quantitively measure the (dis)similarities of the production behaviors.

Automated summary

Manufacturers struggle to predict rare events using data from multiple products production lines, and there is little research on quantitatively selecting training data. This study proposes a training data selection method to improve the performance of deep learning models, which can measure the similarities between multivariate time series using categorical variables.