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国外文献 > 1.轴承 > 5. 监测与诊断 > 故障诊断

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标 题： 2-Steps Approach for Both Rotor and Bearing Faults Identification in Rotating Machines Using Measured Vibration Responses
利用测得的振动响应识别旋转机械转子和轴承故障的两步法
作 者： Khalid M. Almutairi
Jyoti K. Sinha
关键词： Rotating machines  [旋转机械]
Vibration analysis  [ 振动分析]
Machine learning  [ 机器学习]
Rotor faults  [ 转子故障]
Bearing faults  [ 轴承故障]
出版年： 2024
文集名： Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023), Volume 1
联合国大会、DAMAS、IncoME和TEPEN会议联合会议（UNIfied 2023）会议纪要，第一卷
会议名： 2023 International Conference on Damage Assessment of Structures (DAMAS 2023), 2023 International Conference on Maintenance Engineering (IncoME 2023), 2023 Conference of the Efficiency and Performance Engineering Network (TEPEN 2023)
2023年结构损伤评估国际会议（DAMAS 2023）、2023年维护工程国际会议（IncoME 2023）、2023年效率与性能工程网络会议（TEPEN 2023）
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摘 要： An earlier study has proposed the 2-Steps approach for the rotor-related faults only using the measured vibration responses on the bearing housings on a rotating rig. The method has used the vibration parameters in both time and frequency domains calculated from the measured vibration responses at each bearing and the artificial neural network (ANN) based machine learning (ML) models to identify the machine health condition. Step-1 determines whether the machine is healthy or faulty. Then, in Step-2, only faulty conditions data are used to identify the exact nature of the faults. In the current study, this 2-Steps Approach is further extended and examined to both rotor and anti-friction bearing faults. The vibration parameters are revised to include bearing-related faults. This exercise has been done on the measured vibration data from a laboratory-scaled rotating rig. The extended study presents new insights and findings built on the previous work. This study can be a valuable contribution to predictive maintenance. It demonstrates its effectiveness in diagnosing faults in rotating machines, reducing the risk of failure, and enhancing reliability in industrial operations.

此前的一项研究提出了仅基于旋转设备上轴承壳体测得的振动响应，针对转子相关故障的两步法。该方法利用从每个轴承测得的振动响应计算出的时间域和频率域振动参数，并结合基于人工神经网络（ANN）的机器学习（ML）模型，来判断设备的健康状态。第一步用于判断设备是正常还是出现故障； 第二步则仅使用故障数据，以确定故障的具体性质。在本研究中，该两步法进一步扩展并应用于转子和抗摩擦轴承的故障检测。振动参数被更新，纳入了与轴承相关的故障信息。该研究基于实验室规模旋转设备的测得振动数据进行验证。这项扩展研究在以往工作基础上提供了新的见解和发现。本研究可为预测性维护提供有价值的贡献，有效展示了其在诊断旋转机械故障方面的应用效果，有助于降低故障风险，提升工业运行的可靠性。
中文内容为网络AI翻译，专业术语未必准确，仅供参考。

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