Structural health monitoring systems consist of a method to measure the structure's performance at a given point in time and a means to interpret the measured raw data in terms of presence and location of damage. This work focuses on the interpretation of the measured raw data. The concept of structural health monitoring that this work supports is a multi-stage damage identification system where the first, or screening, stage is used to indicate when further inspection is necessary, prior to the degradation of structural parameters. Finding low levels of damage is important for this screening stage. This paper describes the application of a previously presented wavelet based algorithm to detect relatively low levels of structural damage. Typically strain sensor data is used in the algorithm. However, the algorithm does not depend on a specific type of data (strain, stress, displacement, etc.), the conditions (load, boundary, etc.) under which the data was acquired, or the geometry of the structure. The structures examined in this paper are steel plates. Sensor data is either obtained from experimentation or finite element models. In the case of finite element analyses, sensor data in the form of non-linear time histories is extracted, thus producing the equivalent of raw sensor data. The wavelet based algorithm makes use of the continuous wavelet transformation and examines how this feature changes as damage accumulates.
In the current work, an algorithm based on wavelet transformations is used to detect damage prior to the degradation of structural parameters of plates. Plates are of interest because they are basic building blocks for many structures, such as naval ships. Damage to the connecting joints and in the form of through thickness cracks, along with material and temperature variations are included in the analysis. Dynamic loading conditions consisting of impulse and chaotic oscillations are examined. The wavelet algorithm is applied to sensor data in the form of time histories (e.g. strain). These histories are either measured experimentally or generated via finite element analysis. Sensitivity, robustness, and potential uses of this wavelet algorithm for damage detection are discussed.
All structures, natural and man-made, accumulate damage over their lifetime. The concern is: when does the level of damage interfere with safe performance. In the extreme, catastrophic failure is a clear indication of unsafe levels of damage. One of the goals of health monitoring systems is to identify damage long before this final level of criticality is obtained. Algorithms are required that identify damage prior to the degradation of structural parameters. In the current work several identification algorithms are examined for use on plate structures. Plates are of interest because they are basic building blocks for many structures. Even simple flat plates exhibit complex structural response such as anticlastic bending. Damage is included in the computational study presented in the form of damage to the connecting joints. Trends in local and global response are evaluated. Natural frequency, Fourier, power spectrum and wavelet interrogations are evaluated. Dynamic loading conditions consisting of impulse and chaotic oscillations are examined. Interrogations are performed on displacement and strain histories. Sensitivity and potential uses of these interrogators for damage identification are discussed.