The modal identification of rotating machinery in operation remains a critical challenge relevant for transport, renewable energy, and many other mechanical engineering areas. The rotation superimposes the stochastic ambient excitation and harms the identification process if not properly identified. Traditional approaches struggle with harmonic interference from auxiliary systems, and time-varying operating conditions. These challenges become particularly acute when implementing real-time monitoring systems for predictive maintenance. Despite these advances, a significant gap persists between laboratory validation and industrial implementation. This session bridges this divide by showcasing new approaches, techniques, and real-world applications that bring together the art of Operational Modal Analysis with the dynamic characterization of rotating machines in operation to ensure operation reliability.

Recent advancements in artificial intelligence and machine learning are revolutionizing our approach to these complex problems and are equally welcome in this session. AI-enhanced algorithms might be able to automatically distinguish between true modal responses and harmonic interferences. The combination of classical operational modal analysis with machine-learning could contribute to the development of digital twins and thus the reliable estimation of an operating structure’s health.

Structural health monitoring represents an ideal application for operational modal analysis. A reliable modal parameter identification of rotating machinery in operation is thus essential for effective structural health management. The derived dynamic models can then represent a crucial part of digital twin systems, providing the real-time structural performance data necessary for informed maintenance decisions.

Keywords: Rotating Machinery Diagnostics, Modal Parameter Identification, Structural Health Monitoring, Harmonic Interference Suppression, Machine Learning for OMA, New Sensor Technology

Track chairs

Mona Amer, The University of British Columbia

Dmitri Tcherniak, HBK – Hottinger Brüel & Kjær