A new method for beam-damage-diagnosis using adaptive fuzzy neural structure and wavelet analysis

• We propose a new model for beam-damage locating method.
• We establish a new model using both adaptive fuzzy neural network structure and wavelet transform.
• An experimental investigation is undertaken in order to demonstrate the effectiveness of the proposed method.
• A single damage and double damage are considered in experiment.
• It is demonstrated that the proposed method can predict the damage more accurately than conventional method.

In this work, we present a new beam-damage-locating (BDL) method based on an algorithm which is a combination of an adaptive fuzzy neural structure (AFNS) and an average quantity solution to wavelet transform coefficient (AQWTC) of beam vibration signal. The AFNS is used for remembering undamaged-beam dynamic properties, while the AQWTC is used for signal analysis. Firstly, the beam is divided into elements and excited to be vibrated. Vibrating signal at each element, which is displacement in this work, is measured, filtered and transformed into wavelet signal with a used-scale-sheet to calculate the corresponding difference of AQWTC between two cases: undamaged status and the status at the checked time. Database about this difference is then used for finding out the elements having strange features in wavelet quantitative analysis, which directly represents the beam-damage signs. The effectiveness of the proposed approach which combines fuzzy neural structure and wavelet transform methods is demonstrated by experiment on measured data sets in a vibrated beam-type steel frame structure.