Hydraulic system is the core component of control and power transmission equipment.It is widely used in modern industrial production machinery.Improving the accuracy of hydraulic system fault diagnosis has important engineering significance such as improving engineering efficiency and ensuring work safety.Once faults occur,multiple faults occur at the same time,the traditional BP neural network fault diagnosis system cannot meet the diagnostic accuracy.A method of hydraulic system diagnosis was proposed based on genetic algorithm optimizing the BP neural network (GA-BP).Aiming at three typical fault modes of multi-sensor information fusion hydraulic system under different sampling frequencies,the comparative analysis was made.The results show that the GA-BP fault diagnosis algorithm has better diagnostic performance than the traditional BP neural network.