This book gathers the latest advances, innovations, and applications in the field of condition monitoring, damage assessment, and maintenance engineering, as presented by leading international researchers and engineers at the UNIfied Conference of DAMAS (International Conference on Damage Assessment of Structures), International Conference on Maintenance Engineering (IncoME), and The Efficiency and Performance Engineering (TEPEN), held in Zhangjiajie, China, on May 16-19, 2025. Topics include machinery dynamics, sensors and measurement systems, signal processing, structural damage identification and machine fault diagnosis, plant maintenance and reliability, machine health monitoring, asset management, life-cycle cost optimization, prognostics and health management, maintenance performance measurement, manufacturing process monitoring, and robot-based monitoring and diagnostics. The contributions, which were selected through a rigorous international peer-review process, share exciting ideas that will spur novel research directions and foster new multidisciplinary collaborations.

Presents recent trends in maintenance engineering, condition monitoring, and reliability Offers essential insights into a wide range of topics Written by leading experts in the field

Inhalt

A machine learning based multi-dimensional equipment-level spare parts demand forecasting and support algorithm.- Contribution Title Temperature Compensation Extended Kalman Filter for Battery State of Charge Estimation via Recursive Least Squares Algorithm.- Confidential Computing Graph Similarity Protocol under the Malicious Model and its Applications in Blockchain.- Research on Fault Feature Extraction Method for Helicopter Main Reducer Based on CECP Approach.- Fault Detection in Offshore Wind Turbine Blades Based on Object Detection Techniques.- Normalized RMS for rotating machinery condition monitoring with the use of operation condition data.- Fault Feature Enhancement of Rolling Bearings Based on an Adaptive ENEMD.- Community Detection Method Based on Multi-view Contrastive Mechanism.- A VMD-Transformer Framework for Voltage Degradation Prediction in Proton Exchange Membrane Fuel Cells.