Richard Loendersloot

Richard Loendersloot is an Associate Professor specializing in dynamics-based maintenance, focusing on structural integrity diagnostics and prognostics. His work integrates advanced sensors and data-driven methods for predictive maintenance in composite materials and infrastructure systems.

Research interests include structural health monitoring (SHM), vibration analysis, and damage accumulation modeling. He has contributed to methodologies like Bayesian filtering for prognostics and non-collinear wave mixing for material aging assessment.

Recent publications (2024-2025) emphasize sensor technology comparisons, ML-assisted frameworks for cyber-physical systems, and scaled bridge testing for vehicle-bridge interaction models.

He collaborates widely, with activities including invited talks on smart maintenance and presentations at international conferences. Supervised 13 academic works, though student names are not listed.

Labs/Teams: Part of dynamics-based maintenance research groups, contributing to datasets like motor current/vibration monitoring for e-motor-driven pumps.