The damage of a derrick steel structure affects its bearing safety. In order to identify the damage location quickly and accurately

a method for identifying the damage of a derrick steel structure based on the BP neural network optimized with wavelet packet and genetic algorithm is proposed. Firstly

the excellent performance of the wavelet packet that processes non-stationary vibration signals is used to extract the original vibration signal features

and the information that characterizes the damage is obtained. Then

the data set is established through characteristic parameters to train and test the derrick steel structural damage identification method. Combined with the characteristics of the genetic algorithm

the identification method reduces the shortcomings of the traditional BP neural network. The identification method can determine the damage location without comparing the data characteristics before damage. The experimental results show that the accuracy of the derrick steel structural damage identification method is more than 90%

being higher than that of the traditional BP neural network.