In this paper, pseudomodal properties obtained from the FRF have been calculated from vibration signal measured from a population of cylindrical shells. These properties have been used as inputs into a TS neuro-fuzzy model. A model selection process reveals that the optimum number of fuzzy rules for accurate classification is four as it allows for a high accuracy with a low variance. The TS neuro-fuzzy model classifies the faults in the cylindrical shells with an accuracy of 91.42%, which is an improvement over what the probabilistic neural network has been able to do in the past.

## Acknowledgments

The authors gratefully acknowledge the financial contributions and support obtained from the National Research Fund (NRF), Department of Science and Technology (DST) and the Central Building Research Institute (CBRI). It is through the funding of both this institutions that this project was possible. 

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