Integrating the motor and driver into the confined space of an underwater thruster’s sealed shell can lead to current sensor failures, primarily due to high temperatures, pressures, and electromagnetic interference. Despite progress in distinguishing sensor malfunctions from propeller issues, a significant gap exists in understanding simultaneous sensor and propeller failures. This study addresses this by thoroughly analyzing the propeller’s condition during sensor failure. We introduce two virtual sensors, ingeniously derived from the motor’s voltage and mechanical model, to estimate the thruster’s current from different perspectives and effectively separate sensor and propeller faults. Recognizing the potential discrepancies between the virtual and real sensors, we developed a multi-source signal common features extractor inspired by transfer learning. This extractor obtains common features from measured and estimated currents, leveraging these variations to detect and assess faults accurately. The effectiveness of this approach has been corroborated through simulation and experiment, demonstrating the ability to distinguish between sensor and propeller faults and accurately evaluate the system’s status.

中文翻译：

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基于多源信号共性特征的水下推进器混合故障诊断方法


将电机和驱动器集成到水下推进器密封外壳的有限空间中可能会导致电流传感器故障，这主要是由于高温、压力和电磁干扰。尽管在区分传感器故障和螺旋桨问题方面取得了进展，但在理解传感器和螺旋桨同时发生故障方面仍存在很大差距。本研究通过彻底分析传感器故障期间螺旋桨的状况来解决这个问题。我们引入了两个虚拟传感器，巧妙地从电机的电压和机械模型中衍生出来，从不同的角度估计推进器的电流，并有效地分离传感器和螺旋桨的故障。认识到虚拟传感器和真实传感器之间的潜在差异，我们受迁移学习的启发，开发了一种多源信号共同特征提取器。该提取器从测量和估计的电流中获取共同特征，利用这些变化来准确检测和评估故障。该方法的有效性已通过仿真和实验得到证实，证明了区分传感器和螺旋桨故障并准确评估系统状态的能力。