A Low-Code Visual Framework for Deep Learning-Based Remaining Useful Life Prediction

• Published in: Processes Vol. 13; no. 8; p. 2366
• Main Authors: Lin, Yuhan, Chen, Jianhua, Chen, Sijuan, Nie, Yunfei, Wang, Ming, Zhang, Bing, Yang, Ming, Wang, Jipu
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2025
• Subjects: Algorithms; Complexity; Computer programming; Data analysis; Data processing; Data science; Deep learning; Design; Factories; Intelligent manufacturing systems; Life prediction; Linear programming; Machine learning; Mathematical models; Methods; Neural networks; Optimization; Prediction models; Repair & maintenance; Useful life; Visual discrimination learning; Visualization
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr13082366

In the context of intelligent manufacturing, deep learning-based remaining useful life (RUL) prediction has become a research hotspot in the field of Prognostics and Health Management (PHM). The traditional approaches often require strong programming skills and repeated model building, posing a high entry barrier. To address this, in this study, we propose and implement a visualization tool that supports multiple model selections and result visualization and eliminates the need for complex coding and mathematical derivations, helping users to efficiently conduct RUL prediction with lower technical requirements. This study introduces and summarizes various novel neural network models for DL-based RUL prediction. The models are validated using the NASA and HNEI datasets, and among the validated models, the LSTM model best met the requirements for remaining useful life (RUL) prediction. In order to achieve the low-code usage of deep learning for RUL prediction, the following tasks were performed: (1) multiple models were developed using the Python (3.9.18) language and were implemented on the PyTorch (1.12.1) framework, providing users with the freedom to choose their desired model; (2) a user-friendly and low-code RUL prediction interface was built using Streamlit, enabling users to easily make predictions; (3) the visualization of prediction results was implemented using Matplotlib (3.8.2), allowing users to better understand and analyze the results. In addition, the tool offers functionalities such as automatic hyperparameter tuning to optimize the performance of the prediction model and reduce the complexity of operations.