SS 07
现代复杂工程系统，例如航空航天系统、自主智能系统、工业设备和网络物理系统，正变得日益复杂且相互关联。确保其可靠性和运行安全需要先进的技术来实现及时故障检测、准确诊断和可靠预测。
人工智能 (AI)、机器学习和数据驱动建模的最新进展显著提升了智能故障诊断和预测的能力。基于人工智能的方法使系统能够从海量运行数据中学习复杂模式，从而增强故障检测、故障隔离、健康评估和剩余使用寿命预测的能力。在这些方面尽管取得了一些进展，但仍存在诸多挑战，包括标记故障数据有限、系统复杂性、异构传感信息以及对稳健且可解释的诊断模型的需求。应对这些挑战对于提高复杂工程系统的可靠性和韧性至关重要。
本次专题研讨会旨在为研究人员和实践者提供一个平台，展示智能故障诊断和预测领域的最新进展。本次会议将重点介绍新型人工智能驱动方法、理论发展和实际应用，这些方法和应用能够增强复杂工程系统的可靠性和健康监测能力。
征稿主题包括但不限于：
用于故障诊断和预测的人工智能
数据驱动的故障检测和诊断
测试、传感和诊断
剩余使用寿命 (RUL) 预测
健康状态评估
基于模型和数据驱动的混合诊断与容错方法
用于智能故障检测的多传感器数据融合
用于故障诊断和预测的可解释人工智能
鲁棒且考虑不确定性的诊断与预测方法
复杂工程系统的智能监测
在航空航天、自主系统和工业设备中的应用
Modern complex engineering systems, such as aerospace systems, autonomous intelligent systems, industrial equipment, and cyber-physical systems, are becoming increasingly sophisticated and interconnected. Ensuring their reliability and operational safety requires advanced techniques for timely fault detection, accurate diagnosis, and reliable prognostics.
Recent advances in artificial intelligence (AI), machine learning, and data-driven modeling have significantly improved the capability of intelligent fault diagnosis and prognostics. AI-based approaches enable systems to learn complex patterns from large volumes of operational data, providing enhanced capabilities for fault detection, fault isolation, health assessment, and remaining useful life prediction. Despite these advances, many challenges remain, including limited labeled fault data, system complexity, heterogeneous sensing information, and the need for robust and interpretable diagnostic models. Addressing these challenges is critical for improving the reliability and resilience of complex engineering systems.
This special session aims to provide a platform for researchers and practitioners to present recent advances in intelligent fault diagnosis and prognostics. The session will highlight novel AI-driven methods, theoretical developments, and practical applications that enhance the reliability and health monitoring capabilities of complex engineering systems.
Topics of interest include, but are not limited to:
Artificial Intelligence for Fault Diagnosis and Prognostics
Data-driven Fault Detection and Diagnosis
Testing, Sensing and Diagnosis
Remaining Useful Life (RUL) Prediction
Intelligent Health State Assessment
Hybrid Model-based and Data-driven Fault Diagnosis and Tolerant Control Methods
Multisensor Data Fusion for Intelligent Fault Detection
Explainable AI for Fault Diagnosis and Prognostics
Robust and Uncertainty-aware Diagnosis and Prognostics Methods
Intelligent Monitoring of Complex Engineering Systems
Applications in Aerospace, Autonomous Systems, and Industrial Equipment
