Fig. 11 Progression over time of the proportionality factor of the left-hand sensor (trailing edge) | Fig. 12 Progression over time of the proportionality factor of the right-hand sensor (leading edge) |
Fig. 3 Active sensor unit of the deflection sensor | Fig. 4 Fastening point in the blade (measurement point) |
Fig. 5 HBM deflection sensor with measurement electronics installed |
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The performance requirements for the methodology of a damage early warning or monitoring system are no less demanding, compared with the requirements for the measurement technology being used. Basically, there must be no false alarms. It is not economically viable to be shut down because of repair or maintenance work, and this should be avoided. The system must also give the operator sufficient time to react to damage. This means that the damage must be detected early enough, and its potential threat determined. It must be possible to conclude whether the turbine should be shut down immediately, or whether there is still time available for monitoring. The approach to damage early warning presented here involves a global SHM method.
"Compared to electrical strain gages, fiber-optic strain gages have far better fatigue strength." Dipl.-Ing. Stephan Zerbst is a Scientific Assistant and Coordinator of the Services & Measurement Techniques department at the Institute of Structural Analysis (ISD) of Leibniz University in Hanover.
This means that the entire rotor blade in monitored by a minimum number of sensors, in order to detect the global status of the structure. The sensors no longer work as hotspot-monitors, but are viewed together, as a group [7, 8]. The “proportionality method” comes into play here. This is a method that makes use of the proportionality of the maximum vibration rate and maximum dynamic strain or stress during natural vibration [6].
These two correlating measurands are detected and compared, subject to the direction of natural vibration that is excited at defined locations on the blade structure. In this case, the vibration rate amplitude is differentiated from the deflection signal. The reference status of the undamaged structure with reference factor “pSystem” that was detected in the beginning is subsequently always compared with a newly determined “pSystem” proportionality factor. If the observed proportionality factor is permanently away from its starting level, this indicates the commencement of damage. The “pSystem” proportionality factor is a system factor that includes all the properties of the structure, such as its material properties, storage conditions, cross-sectional shape, etc. This factor is thus an excellent choice as a damage indicator.
Wind Turbine Testing with HBM Opto-electrical interrogators
[1] Patent pending PCT/EP 2008/000942
[2] Hoffmann, K.: "Eine Einführung in die Technik des Messens mit Dehnungsmessstreifen" (An introduction to measurement using strain gages) Hottinger Baldwin Messtechnik, Darmstadt.
[3] VDI/VDE/GESA 2660: Experimentelle Strukturanalyse; optischer Dehnungssensor, basierend auf Faser-Bragg-Gitter. Grundlagen sowie Kenngrößen und deren Prüfung (Experimental structural analysis; optical strain sensor, based on a fiber Bragg grating. Basics, characteristic quantities and their testing).
[4] Hottinger Baldwin Messtechnik GmbH product catalog. Experimental stress analysis.
[5] Haase, K.-H.: AIAS – Associazione Italiana per l‘analisi delle sollecitazioni XXXVIII Convegno nazionale, 2009, Politenico di Torino: Benefits of Using Fiber Optics Strain Gages in Experimental Stress Analysis.
[6] Gasch, R.: Eignung der Schwingungsmessung zur Ermittlung der dynamischen Beanspruchung in Bauteilen, Berlin 1968 (Suitability of vibration measurement for determining dynamic stress in structural elements).
[7] Zerbst, S.; Haake, G.; Reetz, J.; Lynch, J.; Rolfes, R.: Integral SHM-System for Offshore Wind Turbines using Smart Wireless Sensors, Proceedings of the 6th International Workshop of Structural Health Monitoring 2007, Volume 2, p. 1889-1896, San Francisco, Sept. 11-14, 2007.
[8] R. A. Swartz, J. P. Lynch, B. Sweetman, R. Rolfes and S. Zerbst: Structural Monitoring of Wind Turbines using Wireless Sensor Networks, Smart Structures and Systems 6, pp. 183-196, 2010.