1 citations · 1 across the 2 of their papers we have counts for
5 papers
Preliminary Exploration of Large Models Based on Self-Supervised Learning for Health Monitoring of Power Electronic Converters
Xinyuan Liao, Xiangtian Deng, Yang Liu +3
Current artificial intelligence (AI) methods for power electronics converters make limited use of the abundant unlabeled data generated during real-world operation. This underexplo…
Data-Efficient Motor Condition Monitoring with Time Series Foundation Models
Deyu Li, Xinyuan Liao, Shaowei Chen +1
Motor condition monitoring is essential for ensuring system reliability and preventing catastrophic failures. However, data-driven diagnostic methods often suffer from sparse fault…
Parallelizable Complex Neural Dynamics Models for PMSM Temperature Estimation with Hardware Acceleration
Xinyuan Liao, Shaowei Chen, Shuai Zhao
Accurate and efficient thermal dynamics models of permanent magnet synchronous motors are vital to efficient thermal management strategies. Physics-informed methods combine model-b…
PE-TSFM: Self-Supervised Time-Series Learning for Generalizable Power Converter Health Monitoring under Unseen Conditions
Xinyuan Liao, Xinyue Zhang, Xing Wei +4
Data-driven health monitoring of power converters remains limited by poor generalization to unseen operating conditions. This work addresses this out-of-distribution (OOD) challeng…
PE-GPT: A Physics-Informed Interactive Large Language Model for Power Converter Modulation Design
Fanfan Lin, Junhua Liu, Xinze Li +4
This paper proposes PE-GPT, a custom-tailored large language model uniquely adapted for power converter modulation design. By harnessing in-context learning and specialized tiered…