Detection of structural damage using novelty detection algorithm under variational environmental and operational conditions

Novelty detection is a widely used algorithm in different fields of study due to its capabilities to recognize any kind of abnormalities in a specific process in order to ensure better working in normal conditions. In the context of Structural Health Monitoring (SHM), this method is utilized as damage detection technique because the presence of defects can be considered as abnormal to the structure. Nevertheless, the performance of such a method could be jeopardized if the structure is operating in harsh environmental and operational conditions (EOCs). In this paper, novelty detection statistical technique is used to investigate the detection of damages under various EOCs. Experiments were conducted with different scenarios: damage sizes and shapes. EOCs effects were simulated by adding stochastic noise to the collected experimental data. Different levels of noise were studied to determine the accuracy and the performance of the proposed method. (paper)