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Using empirical mode decomposition scheme for helicopter main gearbox bearing defect identification

机译:基于经验模态分解的直升机主变速箱轴承缺陷识别

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Vibration sensors for helicopter health and condition monitoring have been widely employed to ensure the safe operation. Through the years, vibration sensors are now commonly placed on helicopters and have claimed a number of successes in preventing accidents. However, vibration based bearing defect identification remains a challenge since bearing defects signatures are usually contaminated by background noise resulting from variable transmission paths from the bearing to the receiving externally mounted vibration sensors. In this paper, the empirical mode decomposition (EMD) scheme was utilized to analyze vibration signal captured from a CS29 Category `A' helicopter main gearbox, where bearing faults were seeded on one of the planetary gears bearing of the second epicyclic stage. The EMD scheme decomposed vibration signal into a number of intrinsic mode functions (IMFs) for subsequent envelope analysis. The selection of appropriate IMFs to characterize bearing fault signatures was discussed. The analysis result showed that the bearing fault signatures were successfully characterized and revealed the efficacy of the EMD scheme.
机译:用于直升机健康和状态监测的振动传感器已被广泛采用,以确保安全运行。多年来,振动传感器现在通常放置在直升机上,并在防止事故方面取得了许多成功。然而,基于振动的轴承缺陷识别仍然是一个挑战,因为轴承缺陷特征通常会受到背景噪声的污染,该背景噪声是从轴承到接收外部安装的振动传感器的可变传输路径所导致的。在本文中,经验模态分解(EMD)方案用于分析从CS29类“ A”型直升机主齿轮箱捕获的振动信号,其中轴承故障被植入第二个行星齿轮级的行星齿轮轴承中。 EMD方案将振动信号分解为许多固有模式函数(IMF),用于后续的包络分析。讨论了用于表征轴承故障特征的合适IMF的选择。分析结果表明,已经成功地表征了轴承故障特征,并揭示了EMD方案的有效性。

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