Active fault diagnosis for immersed structure

The immersed system environment makes delicate their diagnosis. Such systems encountered in energy production sites (stream turbines), water treatment or oil platform are not easily accessible for diagnosis issues. Most of dedicated diagnosis techniques are expensive, need a precise positioning of sensors and present large delay. The need to develop alternative techniques is therefore justified. The proposed contribution is a part of this approach. It is based on active fault diagnosis and detection. The measurements are performed by ultrasonic echography. The proposed method combines signal-processing tools to characterize the measurements and artificial neural networks (ANNs) for classification and decision.