水润滑推力轴承作为潜水电机的重要组成部件常由于工作环境恶劣、过载和冷热交替,轴承结构磨损而进入异物。针对在潜水电机内拆装加速度传感器困难，水阻尼污染振动信号采集等问题，提出故障状态下潜水电机的数学模型，对仿真得到电流信号进行时移降噪，升维预处理，基于AlexNet模型训练不同状态下电机电流信号（MCS）。经验证，模型能以较高的准确率快速识别故障信号，具有较强的鲁棒性，满足故障诊断预见性和实时性要求。

The water-lubricated thrust bearing works as an important component in the submersible motor. The water-lubricated thrust bearing is often damaged by a poor working environment, overload, and alternating heat and cold resulting in failure of the sealing structure. The disassembling and assembling the acceleration sensor in the submersible motor is difficult and the acquisition of vibration signals is influenced by water damping pollution. To solve these problems, a mathematical model of the submersible motor under fault conditions is proposed, and the current signal is obtained by simulation. Based on the AlexNet model, the motor current signal (MCS) in different states is trained. The simulation results verify that the model can quickly identify fault signals with high accuracy, has strong robustness, and meets the requirements of predictability and real-time performance of fault diagnosis.

国家自然科学基金项目（51977055）；安徽省科技重大专项项目（18030901036，201903a05020042）