国外文献 > 1.轴承 > 1. 基础理论与机制 > 承载与性能
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| 标 题： |
A machine learning-based approach for detection of whirl instability and overheating faults in journal bearings using multi-sensor fusion method
一种基于机器学习的方法，通过多传感器融合技术来检测滚动轴承中的涡动不稳定和过热故障 |
| 作 者： |
Golmohammadi, A.
Safizadeh, M. S. |
| 关键词： |
Journal bearing [滚动轴承]
Oil whirl [ 油涡旋]
Overheating [ 过热]
Condition monitoring [ 状态监测]
Sensor fusion [ 传感器融合]
Load cell sensor [ 称重传感器]
Classification [ 分类]
THERMOHYDRODYNAMIC ANALYSIS [ 热流体动力学分析]
ROTOR [ 转子]
TEMPERATURE [ 溫度]
STABILITY [ 稳定性]
DIAGNOSIS [ 诊断]
SYSTEMS [ 系統]
SPEEDS [ 速度] |
| 出版年： |
2023 |
| 期刊名： |
Journal of the Brazilian Society of Mechanical Sciences and Engineering
《巴西机械科学与工程学会杂志》 |
| 年卷期： |
2023, vol.45, no.3 |
| 摘 要： |
This study proposes a novel multi-sensor fusion-based monitoring technique for the detection of overheating and oil whirl instability faults. By using this method, multi-faults can be detected as soon as possible with the minimum number of sensors with high accuracy. This technique of monitoring uses two eddy current-type proximity probes in X-Y configuration and an embedded load cell in the housing under the Babbitt layer for measuring the fluctuations of oil pressure load. A test rig consisting of a rotor with a hydrodynamic journal bearing was built. For collected data, the parameters in the time and frequency domain are extracted for four conditions including health, oil whirl fault, overheating fault, and both faults simultaneously. In this study, the t-distributed stochastic neighbor embedding (t-SNE) method is used for feature reduction, and then, obtained data are classified by a multi-class Naive Bayes classification model. Finally, the accuracy and sensitivity of the classifier are investigated and concluded that the proximity probe sensor is useful for the detection of overheating and oil whirl faults with high accuracy, but the load cell sensor just can accurately detect oil whirl fault. Accordingly, the proximity probe sensor can be used for overheating fault detection without a thermocouple sensor. Although load cell cannot detect overheating, it can be used for oil whirl fault detection.
本研究提出了一种基于多传感器融合的新型监测技术，用于检测过热和油涡动不稳定故障。通过使用这种方法，可以以最少的传感器数量并以高精度尽快检测出多种故障。这种监测技术使用两个 X-Y 布局的涡流型接近探头以及位于巴比特层下方外壳中的嵌入式测力传感器来测量油压负载的波动。构建了一个由带有液力滚动轴承的转子组成的测试装置。对于收集到的数据，从时域和频域中提取了四个条件下的参数，包括健康状况、油涡动故障、过热故障以及两种故障同时发生的情况。在本研究中，使用了 t 分布随机邻域嵌入（t-SNE）方法进行特征降维，然后，对获得的数据使用多类朴素贝叶斯分类模型进行分类。最后，对分类器的准确性和灵敏度进行了研究，并得出结论：接近探头传感器对于检测过热和油涡流故障具有很高的准确性，但称重传感器仅能准确检测油涡流故障。因此，接近探头传感器可用于过热故障检测，而无需使用热电偶传感器。尽管称重传感器无法检测过热，但它可用于油涡流故障检测。 |
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