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New trends of NDT-based condition monitoring of industrial and power plants

 
: Niese, Frank; Jäckel, Patrick; Rabe, Ute; Herrmann, Hans-Georg; Szielasko, Klaus

:
Volltext urn:nbn:de:0011-n-4591125 (6.2 MByte PDF)
MD5 Fingerprint: f07badc82cbb90deab27de11193aa8f0
Erstellt am: 3.8.2017


The British Institute of Non-Destructive Testing -BINDT-, Northampton:
First World Congress of Condition Monitoring, WCCM 2017 : 13-16 June 2017, ILEC Conference Centre, London, UK
Northhampton: BINDT, 2017
9 S.
World Conference of Condition Monitoring (WCCM) <1, 2017, London>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IZFP ()
MoniDAQ; ultrasonic tomographic method; predictive maintenance

Abstract
Energy generation and industrial production sectors require a more exact condition-based structural assessment for predictive maintenance. Condition monitoring can be significantly improved by using new approaches for monitoring concepts including non-destructive testing. Couplant-free ultrasonic using electromagnetic acoustic transducers can be applied for monitoring of metal structures, for example light poles. A major step towards defect quantification will be attained by the development of an innovative ultrasonic tomographic method based on guided waves, which is currently developed at Fraunhofer IZFP. As will be shown, the quantitative determination of corrosion in non-accessible or hidden areas becomes possible, e.g. for pipe supports and under insulation. In addition the potential of ultrasound sampling phased array technology using full-matrix-capture and total focusing technique applied to difficult to inspect materials and structures will be presented. Demonstrators for a permanent condition monitoring are currently being developed at IZFP. One of them is named “MoniDAQ” – a small, robust, cost-effective and self-powered platform concept. Individual inspection modules such as ultrasonic, eddy current or micromagnetic devices can be integrated and subsequent intelligent signal processing will enable a rapid data analysis.

: http://publica.fraunhofer.de/dokumente/N-459112.html