Intelligent maintenance (IM) is a strategy (action or methodology) that uses signal processing, data analysis, and decision support tools to predict and prevent potential failures with the knowledge of the principle of machines or systems. The recent advancements in computer science and technology, information theory, and electronics have facilitated the design and implementation of such IM systems. With the rapid development of artificial intelligence (AI) technology, intelligent maintenance has entered a new stage. In this special track, we would like to share our work on this topic, discuss the role of AI in IM, and promote the integration of AI and IM.
| Name | Title |
|---|---|
| Xiaohang Jin | Prior Knowledge Embedded Networks for Condition Monitoring of Wind Turbine |
| Zhilin Dong | Intelligent Bearing Fault Diagnosis Framework Based on EWT and 1D-ISACNN |
| Wei Fan | Noise Resistant Correlation Method for Bearing Health Management |
| Guanqi Fang | Exploring Initiation-Growth Correlation in Accelerated Degradation Testing Data |
| Wenpo Huang | Statistical Test-based Adversarial Client Detection in Federated Learning under Poisoning Attacks |
| Ziqian Kong | Remaining Useful Life Prediction Based on Spatio-temporal Graph Neural Network |
| Xiuli Wang | Optimized Test Selection for Efficient Fault Detection in Complex Systems |
