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国外文献 > 1.轴承 > 8. 系统与应用 > 系统集成

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标 题： A Comparative Study of the Effectiveness of Adaptive Filter Algorithms, Spectral Kurtosis and Linear Prediction in Detection of a Naturally Degraded Bearing in a Gearbox
自适应滤波算法、频谱偏度与线性预测在齿轮箱中检测自然退化轴承效果的比较研究
作 者： Faris Elasha
Cristobal Ruiz-Carcel
David Mba
Pramesh Chandra
关键词： Vibration  [振动]
Adaptive filter  [ 自适应滤波器]
Signal separation  [ 信号分离]
Bearing diagnostics  [ 轴承诊断]
Gearbox  [ 齿轮箱]
出版年： 2014
期刊名： Journal of Failure Analysis and Prevention
失效分析与预防杂志
年卷期： 2014, vol.14, no.5
摘 要： Diagnosing bearing faults at the earliest stages is critical in avoiding future catastrophic failures. Many techniques have been developed and applied in diagnosing bearings faults; however, these traditional diagnostic techniques are not always successful when the bearing fault occurs in gearboxes where the vibration response is complex; under such circumstances, it may be necessary to separate the bearing signal from the complex signal. In this paper, an adaptive filter has been applied for the purpose of bearing signal separation. Four algorithms were compared to assess their effectiveness in diagnosing a bearing defect in a gearbox, least mean square (LMS), linear prediction, spectral kurtosis and fast block LMS. These algorithms were applied to decompose the measured vibration signal into deterministic and random parts with the latter containing the bearing signal. These techniques were applied to identify a bearing fault in a gearbox employed for an aircraft control system for which endurance tests were performed. The results show that the LMS algorithm is capable of detecting the bearing fault earlier in comparison with the other algorithms.

在轴承故障的早期阶段进行诊断对于避免未来发生严重失效至关重要。许多技术已被开发并应用于轴承故障的诊断中； 然而，当齿轮箱中轴承故障发生且振动响应复杂时，这些传统诊断方法并不总是有效。在这种情况下，可能需要将轴承信号与复合信号分离。本文采用自适应滤波器来实现轴承信号的分离，并比较了四种算法在齿轮箱轴承故障诊断中的有效性：最小均方（LMS）、线性预测、谱曲率和快速分块LMS。这些算法被用于将测得的振动信号分解为确定性和随机性两部分，其中随机部分包含轴承信号。上述技术应用于对一种用于飞机控制系统的齿轮箱进行轴承故障识别，该系统已进行了耐久性测试。结果表明，与其他算法相比，LMS算法能够更早地检测到轴承故障。
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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