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Vibration-based condition monitoring in planetary gearbox via using an internal sensor

机译：使用内部传感器监控行星齿轮箱中基于振动的状态

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With the benefits of strong load-bearing capacity, compact, light weight and large transmission ratio, planetary gearboxes are widely applied in the fields of automobiles, helicopters, wind turbines, etc. However, at harsh working environments, some critical components of planetary gearboxes, such as gears and bearings, are extremely vulnerable to faults after the long-time operation, which may cause the breakdown of the entire machinery system. Vibration analysis is an efficient way for monitoring the machine conditions to avoid the catastrophic failures and facilitate maintenance plans for numerous industrial applications. However, there is still a lack of successful signal processing strategies for the detection and diagnosis of bearing faults of the planetary gearbox. Because planetary gearbox comprises many rotating components that lead to signal contamination by strong background noise. Besides, the transmission path between an externally mounted sensor and fault signal source is time-varying, which means those traditional fault diagnosis methods developed for fixed-axis gearboxes may not work. For the purpose of solving the aforementioned bearing fault detection issues in the planetary gearbox, this thesis utilizes an internally mounted accelerometer for the vibration data collection. This thesis also develops a hybrid signal processing approach to the internal sensor-based measurement. The result shows that, in contrast to the externally mounted sensor, the internal sensor has an overwhelmingly superior performance for the inner race faults detection in the planet bearings. In addition, a novel spectral kurtosis (SK) based demodulation band selection approach is presented in this thesis. This method is introduced to address the electromagnetic interference (EMI) issue from internal sensor-based measurement. It also could be further applied in other industrial cases where intense EMI appears.
机译：行星齿轮箱具有承重能力强，结构紧凑，重量轻，传动比大等优点，广泛应用于汽车，直升机，风力涡轮机等领域。然而，在恶劣的工作环境中，行星齿轮箱的一些关键部件长时间运行后，齿轮和轴承等齿轮极易发生故障，这可能会导致整个机械系统的故障。振动分析是监视机器状况以避免灾难性故障并促进众多工业应用维护计划的有效方法。然而，仍然缺少用于检测和诊断行星齿轮箱轴承故障的成功信号处理策略。因为行星齿轮箱包括许多旋转部件，这些旋转部件会因强烈的背景噪声而导致信号污染。此外，外部安装的传感器与故障信号源之间的传输路径是随时间变化的，这意味着那些为固定轴变速箱开发的传统故障诊断方法可能无法正常工作。为了解决行星齿轮箱中的上述轴承故障检测问题，本文利用内部安装的加速度计来收集振动数据。本文还开发了一种基于内部传感器的混合信号处理方法。结果表明，与外部安装的传感器相比，内部传感器在行星轴承内圈故障检测方面具有压倒性的优越性能。此外，本文提出了一种基于谱峰度（SK）的解调频带选择方法。引入此方法是为了解决基于内部传感器的测量中的电磁干扰（EMI）问题。它也可以进一步应用于出现强烈EMI的其他工业案例。

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