The rotor, as one of the key components of a downhole motor, directly affects the safety, cost, and efficiency of the entire drilling operation. This paper proposes an annular triboelectric sensor (ATES) for monitoring rotor faults in downhole motors, marking an innovative application of triboelectric nanogenerators in the field of downhole fault monitoring. The ATES is characterized by its simple structure, long lifespan, and high-temperature resistance, making it particularly suitable for the complex conditions of downhole environments. The ATES can also monitor radial vibrations of downhole tools in real time and, when combined with the ResNet-18 algorithm, can accurately identify rotor imbalances, misalignments, and rubbing faults, achieving a classification accuracy of up to 100 %. Additionally, this paper presents an intelligent offline analysis system for downhole rotor fault diagnosis, which integrates deep learning and visualization techniques. This system efficiently identifies rotor faults and outputs visual results, providing drillers with intuitive diagnostic references, thereby significantly improving the efficiency and accuracy of fault diagnosis. Overall, the ATES offers a viable pathway for developing new downhole intelligent sensing devices and technologies.

中文翻译：

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使用深度学习和环形摩擦电传感器监测井下电机转子故障


转子作为井下电机的关键部件之一，直接影响整个钻井作业的安全性、成本和效率。本文提出了一种用于监测井下电机转子故障的环形摩擦电传感器 （ATES），标志着摩擦纳米发电机在井下故障监测领域的创新应用。ATES 具有结构简单、使用寿命长、耐高温等特点，特别适用于井下环境的复杂条件。ATES 还可以实时监测井下工具的径向振动，并与 ResNet-18 算法结合使用时，可以准确识别转子不平衡、错位和摩擦故障，实现高达 100% 的分类精度。此外，本文还提出了一种用于井下转子故障诊断的智能离线分析系统，该系统集成了深度学习和可视化技术。该系统可有效识别转子故障并输出可视化结果，为钻探人员提供直观的诊断参考，从而显著提高故障诊断的效率和准确性。总体而言，ATES 为开发新的井下智能传感设备和技术提供了一条可行的途径。