国外文献 > 1.轴承 > 2. 轴承类型与部件 > 核心部件
关键词搜索：
| 标 题： |
A machine learning-based approach for detection of whirl instability and overheating faults in journal bearings using multi-sensor fusion method
基于机器学习的方法，利用多传感器融合技术检测滚柱轴承中的涡流不稳定和过热故障 |
| 作 者： |
Golmohammadi, A.
Safizadeh, M. S. |
| 关键词： |
Journal bearing [圆柱滚子轴承]
Oil whirl [ 油涡]
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 [ 热流体力学分析]
ROTOR [ 转子]
TEMPERATURE [ 温度]
STABILITY [ 稳定性]
DIAGNOSIS [ 诊断]
SYSTEMS [ 系统]
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期刊 |
| 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不稳定性故障。该方法可在最少数量的传感器下实现高精度、快速发现多种故障。监测技术采用X-Y配置的两个涡流型接近探头，以及位于巴比特层下方壳体内的嵌入式称重传感器，用于测量油压负载的变化。搭建了包含水动力轴向轴承转子的测试装置。对采集的数据，提取出时间域和频域中的参数，涵盖健康状态、油涡故障、过热故障以及两种故障同时发生四种工况。本研究采用t-分布随机邻域嵌入（t-SNE）方法进行特征降维，随后利用多类朴素贝叶斯分类模型对所得数据进行分类。最后，对分类器的准确性和灵敏度进行了研究，结论是：接近式探针传感器可用于检测过热和油涡故障，具有高准确性； 而称重传感器仅能准确检测油涡故障。因此，接近式探针传感器可在不使用热电偶传感器的情况下用于检测过热故障。尽管称重传感器无法检测过热，但可用于检测油涡故障。 |
| 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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