  This paper presents real-time vibration based identification technique using
measured frequency response functions(FRFs) under random vibration loading.
Artificial Neural Networks (ANNs) are trained to map damage fingerprints to
damage characteristic parameters. Principal component statistical analysis(PCA)
technique was used to tackle the problem of high dimensionality and high noise
of data, which is common for industrial structures. The present study considers
Crack, Rivet hole expansion and redundant uniform mass as damages on the
structure. Frequency response function data after being reduced in size using
PCA is fed to individual neural networks to localize and predict the severity
of damage on the structure. The system of ANNs trained with both numerical and
experimental model data to make the system reliable and robust. The methodology
is applied to a numerical model of stiffened panel structure, where damages are
confined close to the stiffener. The results showed that, in all the cases
considered, it is possible to localize and predict severity of the damage
occurrence with very good accuracy and reliability.
