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To study vibration fatigue damage localization of offshore oil and gas pipelines, aiming at the location error caused by uncertainty of the initial parameters in backpropagation (BP) neural network training, an improved BP neural network based on the genetic algorithm (GA) is proposed to locate pipeline damage. This approach was verified by experiments and simulations. First, a BP neural network for structural damage location was constructed, and a method to optimize the BP neural network parameters based on the GA was established. Then, a finite element model was established based on the measured data from modal tests of a physical pipeline model, and a large number of vibration neural network training samples were obtained using the finite element model. Finally, to show that the improved BP neural network based on the GA had a better damage location accuracy, the location results of the original BP and GA BP methods were compared for cases with no noise and with 5% noise. The results show that the average error of the BP neural network based on the GA was less than 3%, which was 11.6% lower than that of the original BP neural network.

A remote sensing gear shaft stress monitoring system is studied to deal with the stress balance of transmission gear, which realizes the real-time dynamic monitoring of the planetary gear shaft stress. The design of the whole system structure, the installation position of the basic system and the special working environment of the gear are carried out; the circuit of each function unit is designed, which is based on the overall index requirement and installation space limitation; the software program design is carried out on the basis of the hardware circuit; The main components of the system and the principle of dynamic monitoring system are described; each functional unit and the whole of the system are tested and analyzed in detail. The operation results show that the design is reliable, the actual functions of each unit can meet the design index, and has a certain contribution in the field of gear shaft stress monitoring.