Multivariate Structural Health Monitoring for Rotor Blades
KI-unterstütztes Schallemissionsmonitoring zur automatischen Schadenserkennung in
Tragstrukturen von Windenergieanlagen
German Federal Ministry for Economic Affairs and Energy (BMWi).
When operating a wind turbine, damage of the rotor blade is a serious problem and has to be taken into account. The rotor blades are subjected to varying dynamic loads during the whole lifespan which is planned to be at least 20 years. The production processes of modern blades is lowly automated with higher fabrication tolerances. Even small damages of the blade can accumulate over time and lead to structural relevant damage. Further the economic interest of higher wind turbines led to hybrid tower constructions with its lower part made out of prestressed concrete. Due to their importance for structural integrity and structural safety, there is a significant interest in the continous monitoring of steel tendons, which may suffer from stress corrosion cracking.
Therefore regular sight inspections are mandatory in many countries. However these inspections cannot provide an instant damage detection. Besides the safety risk of an undetected damage, the economical burdens are increasing rapidly if the damage increases given the costs of repairing, replacing and downtime. A system detecting reliably defects and in early stages helps to react fast and to avoid greater damage. Such a method will enable the wind turbine operator to provide higher operational safety and to minimize the economical burdens.
The aim is to pave the way to a damage detection system which reliably detects damage in early stages. A further goal is to extract useful information about the damage like its location and an estimate of the damage relevance.
One promising approach for early damage detection in steel tendons and rotorblades is the acoustic emission event detection method. In this regard acoustic emission events are stress waves emitted by a damage process. These sudden energy release caused by a damage event results in structureborne as well as airborne sound, which can be detected using mid-to-high frequency structureborne sound sensors and microphones, respectively. The higher risks of lightning strike damage with wired electrical sensors can be avoided by using fiber optic sensors. Measurement campaigns included two full-scale rotorblade tests, operational recordings of structureborne and airborne sound in the tower and the rotorblade of real wind turbines, respectively. With sophisticated signal processing methods and the application of machine learning, environmental noise and non-damage related events can be handled, making the approach robust for operating under real world conditions.