SS 01
With the rapid advancement of industrial digitalization and intelligence, modern engineering systems are becoming increasingly complex, featuring multi-scale, multi-physics, and multi-source heterogeneous data characteristics. Reliability analysis and maintenance are critical to ensuring the safe, stable, and efficient operation of these systems throughout lifecycle, while knowledge-data fusion and AI technologies have emerged as core enablers to address the challenges of traditional reliability analysis and maintenance paradigms. This session focuses on the knowledge-data fusion based multi-scale reliability analysis, and AI-aided maintenance, aiming to explore cutting-edge theories, methodologies, tools, and practical applications of reliability analysis and maintenance. By fusing explicit knowledge and implicit data, this session seeks to enhance the accuracy, adaptability, and efficiency of multi-scale reliability assessment, and promote the innovation of AI-driven maintenance strategies for complex engineering systems. It serves as a platform for researchers and practitioners to exchange the latest achievements, discuss key challenges, and advance the application of knowledge-data fusion and AI technologies in reliability analysis and maintenance engineering.
Topics
Knowledge-data fusion methodologies for multi-scale reliability modeling of complex systems
Knowledge graph-based reliability assessment for multi-component, multi-scale industrial equipment
Deep learning-enabled Prognostic Health Management (PHM) with knowledge-data fusion for equipment maintenance
Multi-scale uncertainty quantification in reliability analysis using knowledge-data fusion and Bayesian methods
Transfer learning-based knowledge reuse for cross-scale reliability prediction and maintenance optimization
AI-aided adaptive maintenance strategies based on multi-scale reliability degradation data
Knowledge-guided data mining for identifying critical factors in multi-scale reliability degradation
Industrial case studies on knowledge-data fusion based multi-scale reliability analysis and AI-aided maintenance
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