钻井设备的故障诊断与预测性维护发展现状与挑战

Development Status and Challenges of Fault Diagnosis and Predictive Maintenance for Drilling Equipment

摘要:

钻井设备的故障诊断与预测性维护是井场安全与生产稳定运行的重要保障。对钻井系统的结构组成及固有的故障特性进行了分析，研究了故障硬件传感器采集的振动、声学、温度等多源信号的故障监测特性与适用性，并探讨了传统时域、频域及时频域信号分析方法。重点讨论了基于深度学习的设备故障诊断技术，特别是在故障诊断中应用的专业模型与大模型。分析了复杂工况下故障诊断所面临的数据治理问题及模型方法的挑战，提出了钻井设备智能故障诊断的未来发展趋势与可实施的研究方向。

Abstract:

Fault diagnosis and predictive maintenance of drilling equipment are critical for ensuring safety and stable operations in oilfields. The structure and inherent fault characteristics of drilling systems were analyzed, focusing on the fault monitoring characteristics and applicability of multi-source signals such as vibration, acoustics, and temperature collected by hardware sensors. Traditional signal analysis methods in the time, frequency, and time-frequency domains were examined. The application of deep learning-based fault diagnosis techniques, particularly specialized models and large models, was discussed. Challenges in data governance and model methods for fault diagnosis under complex working conditions were analyzed, and future development trends and actionable research directions for intelligent fault diagnosis of drilling equipment were proposed.