Failure detection technique under random fatigue loading by machine learning and dual sensing on symmetric structure

Abstract To avoid the sudden failure of mechanical structures under repeated loading and to supplement conventional methods of managing life time, we propose a failure detection technique under random fatigue loading using machine learning and dual sensing on a symmetric structure. The state of a shackle, which is used to connect the cargo to the hoist efficiently, under fatigue loading was collected using two strain sensors of a dual system. The strains were preprocessed and labeled as normal or abnormal. Logistic regression machine learning was employed to determine the decision boundary line. Then, we gathered the decision boundary lines of each experiment for determining the time of failure, and we verified every experiment with the most conservative decision boundary line. The results indicate that failure was detected before the crack occurred and the time to notice maintenance could be controlled.