报告题目： Digital Twin for Rotating Machinery Elements Condition Monitoring and Maintenance Optimization
主讲人：Pradeep Kundu
报告时间：2024年 10月 8 日下午16：00
报告地点：西苑校区南九报告厅
主办单位：机电工程学院
报告摘要：
This presentation addresses the critical issue of monitoring the failure and degradation of the rotating machinery elements. The rotating machinery elements failure lead to unplanned outages, compromised product quality, decreased productivity, and increased operating and maintenance costs. To mitigate these challenges, machine learning (ML) based data-centric models are often employed for health assessment, encompassing anomaly detection, fault diagnostics, and prognostics for predicting remaining useful life. However, the scarcity of training data, especially for unreported damages, poses a significant limitation in the development of these ML models.
Physics-based models, which utilize the understanding of damage progression, offer an alternative by reducing the data requirements of data-driven models. Despite their potential, these models can exhibit high modeling errors due to assumptions and simplicity. The digital twin, a dynamic, virtual representation of a physical asset, presents an opportunity to overcome these limitations and enhance prediction accuracy.
The talk focuses on the development of a robust digital twin model that can address various challenges such as the unavailability of data for all failure modes, the black-box nature of ML models, high modeling uncertainty at the fleet level, and the assumptions inherent in physics-based models. By integrating both data-centric and physics-based approaches, the digital twin can provide a more accurate and reliable prediction framework.
The presentation is structured to first explain the impact of asset failure and degradation on productivity, quality, and costs in smart manufacturing environments. It then delves into the use of ML-based models for health assessment, the limitations posed by insufficient training data, and the role of physics-based models in mitigating these limitations. Finally, it discusses the potential of digital twins technique in practical engineering.
报告人简介：
Pradeep Kundu，比利时鲁汶大学工学院机械工程系助理教授。研究方向：机械系统动力学，数字孪生、状态监测、故障诊断、故障预测、预测性维护、人工智能、可靠性工程。
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