Fault diagnosis on beam-like structures from modal parameters using artificial neural networks

• Damage identification at two points of I-beam structures is developed using ANNs.
• We used the first five natural frequencies and mode shapes to train the ANNs.
• We applied different damage scenarios to generate modal parameters of I-beams.
• We obtained the modal parameters of structures using experimental modal analysis.
• We simulated the damaged beams to obtain the modal parameters using FE modeling.

Currently, visual inspection is performed in order to evaluate damage in structures. This approach is affected by the constraints of time and the availability of qualified personnel. Thus, new approaches to damage identification that provide faster and more accurate results are pursued. A promising approach to damage evaluation and detection utilizes artificial neural networks (ANNs) in solving these two problems. ANNs are a powerful artificial intelligence (AI) technique that have received wide acceptance in predicting the extent and location of damage in structures. In this study, the fundamental strategy for developing ANNs to predict the severity and location of double-point damage cases from the measured data of the dynamic behavior of the structure in I-beam structures is considered. ANNs are trained using vibration data consisting of natural frequencies and mode shapes obtained from experimental modal analysis and finite element simulations of intact and damaged I-beam structures. By using ANNs, some significant problems of conventional damage identification approaches can be overcome and damage detection accuracy can be improved.