Investigation Study of Structure Real Load Spectra Acquisition and Fatigue Life Prediction Based on the Optimized Efficient Hinging Hyperplane Neural Network Model

In the realm of engineering practice, various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra. Thus, accurately predicting the fatigue life of structures becomes notably arduous. This paper proposed an approach to predict the fatigue life of structure based on the optimized load spectra, which is accurately estimated by an efficient hinging hyperplane neural network (EHH-NN) model. The construction of the EHH-NN model includes initial network generation and parameter optimization. Through the combination of working conditions design, multi-body dynamics analysis and structural static mechanics analysis, the simulated load spectra of the structure are obtained. The simulated load spectra are taken as the input variables for the optimized EHH-NN model, while the measurement load spectra are used as the output variables. The prediction results of case structure indicate that the optimized EHH-NN model can achieve the high-accuracy load spectra, in comparison with support vector machine (SVM), random forest (RF) model and back propagation (BP) neural network. The error rate between the prediction values and the measurement values of the optimized EHH-NN model is 4.61%. In the Cauchy-Lorentz distribution, the absolute error data of 92% with EHH-NN model appear in the intermediate range of ±1.65%. Also, the fatigue life analysis is performed for the case structure, based on the accurately predicted load spectra. The fatigue life of the case structure is calculated based on the comparison between the measured and predicted load spectra, with an accuracy of 93.56%. This research proposes the optimized EHH-NN model can more accurately reflect the measurement load spectra, enabling precise calculation of fatigue life. Additionally, the optimized EHH-NN model provides reliability assessment for industrial engineering equipment.