Abstract
Discrimination between three sources of variability in a structural health monitoring system are investigated: environmental or operational effects, sensor faults, and structural damage. Different environmental or operational effects are included in the training data and can be accounted for by the model. Distinguishing between sensor fault and structural damage utilizes the fact that the sensor faults are local, while structural damage is global. A time domain approach is used to model the sensor network and the generalized likelihood ratio test (GLRT) is then used to detect and localize a change in the system. An experimental study is performed to validate the proposed method.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call