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Structural Monitoring and Maintenance   Volume 3, Number 3, September 2016, pages 195-214
Impact identification and localization using a sample-force-dictionary – General Theory and its applications to beam structures
Daniel Ginsberg and Claus-Peter Fritzen

Abstract     [Full Text]
    Monitoring of impact loads is a very important technique in the field of structural health monitoring (SHM). However, in most cases it is not possible to measure impact events directly, so they need to be reconstructed. Impact load reconstruction refers to the problem of estimating an input to a dynamic system when the system output and the impulse response function are usually known. Generally this leads to a so called ill-posed inverse problem. It is reasonable to use prior knowledge of the force in order to develop more suitable reconstruction strategies and to increase accuracy. An impact event is characterized by a short time duration and a spatial concentration. Moreover the force time history of an impact has a specific shape, which also can be taken into account. In this contribution these properties of the external force are employed to create a sample-force-dictionary and thus to transform the ill-posed problem into a sparse recovery task. The sparse solution is acquired by solving a minimization problem known as basis pursuit denoising (BPDN). The reconstruction approach shown here is capable to estimate simultaneously the magnitude of the impact and the impact location, with a minimum number of accelerometers. The possibility of reconstructing the impact based on a noisy output signal is first demonstrated with simulated measurements of a simple beam structure. Then an experimental investigation of a real beam is performed.
Key Words
    impact identification; load reconstruction; ill-posed and inverse problem; sparse recovery; simultaneously impact localization and identification
Daniel Ginsberg and Claus-Peter Fritzen: University of Siegen, Department of Mechanical Engineering, Paul-Bonatz-Strasse 9-11, 57076 Siegen, Germany

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