Reliability based impact localization in composite panels using Bayesian updating and the Kalman filter

- Bayesian updating and the Kalman filter can improve the reliability of SHM systems.
- Mitigating the effect of faulty sensors in SHM systems with uncertainties is desired.
- This can be achieved by fusing ANN (Artificial Neural Network) sub-networks.
- False alarm is inherent to all SHM systems. Minimising false alarm rate is desired.
- This can be achieved with classification ANNs for a small range of impact conditions.

In this work, a reliability based impact detection strategy for a sensorized composite structure is proposed. Impacts are localized using Artificial Neural Networks (ANNs) with recorded guided waves due to impacts used as inputs. To account for variability in the recorded data under operational conditions, Bayesian updating and Kalman filter techniques are applied to improve the reliability of the detection algorithm. The possibility of having one or more faulty sensors is considered, and a decision fusion algorithm based on sub-networks of sensors is proposed to improve the application of the methodology to real structures. A strategy for reliably categorizing impacts into high energy impacts, which are probable to cause damage in the structure (true impacts), and low energy non-damaging impacts (false impacts), has also been proposed to reduce the false alarm rate. The proposed strategy involves employing classification ANNs with different features extracted from captured signals used as inputs. The proposed methodologies are validated by experimental results on a quasi-isotropic composite coupon impacted with a range of impact energies.