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Prediction of Residual Fatigue Life of Bolts Based on Metal Magnetic Memory

In order to predict the residual fatigue life of the bolt by the metal magnetic memory signal, the fatigue tests of the bolt were carried out and the signals of the bolt at different loading cycles were measured. By extracting 48 characteristic parameters as input, a support vector machine (SVM) model is established to predict the residual fatigue life of bolts. The parameters of the model are optimized by using three approaches including grid search, particle swarm and genetic algorithm. The results show that the model has a satisfied accuracy when using genetic algorithm as parameter optimization approach. The model provides a promising way to predict the residual fatigue life of bolts, which is significant for structural integrity management of engineering structures.