Fatigue life prediction of the drilling mast for rotary drilling rig using an improved hybrid algorithm

To address the demand for accurate fatigue life prediction of the drilling mast for rotary drilling rig in engineering, an improved hybrid Aquila-African Vulture Optimization Algorithm (IAOAVOA) is proposed to optimize the BP neural network method for predicting the fatigue life of drill masts. Firstly, the exploration stage of the Aquila Optimizer (AO) and the development stage of the African Vulture Optimization Algorithm (AVOA) are combined, and the improved Tent chaotic mapping strategy and the multi-point Levy improvement strategy are introduced. The BP neural network is optimized to obtain the IAOAVOA-BP prediction model. Finally, the life prediction of the drill mast of a rotary drill rig is accomplished based on the dataset established by ANSYS and compared with other life prediction models. The research results show that the established IAOAVOA-BP rotary drilling rig mast life prediction model has high accuracy compared to the test sample point set. Compared with AVOABP and AOBP, the MAE value, RMSE value, and MAPE value have decreased by 53.57%, 56.52%, 53.89%, 39.71%, 99.9%, and 100%, respectively. The average relative error of IAOAVOABP is only 0.92%. The minimum life of the drill mast occurs near the large disk with a minimum number of cycles of 26,683.