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Polyspectral signal analysis techniques for condition based maintenance of helicopter drive-train system.

机译：用于基于条件的直升机传动系统维护的多光谱信号分析技术。

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摘要

For an efficient maintenance of a diverse fleet of air- and rotorcraft, effective condition based maintenance (CBM) must be established based on rotating components monitored vibration signals. In this dissertation, we present theory and applications of polyspectral signal processing techniques for condition monitoring of critical components in the AH-64D helicopter tail rotor drive train system. Currently available vibration-monitoring tools are mostly built around auto- and cross-power spectral analysis which have limited performance in detecting frequency correlations higher than second order. Studying higher order correlations and their Fourier transforms, higher order spectra, provides more information about the vibration signals which helps in building more accurate diagnostic models of the mechanical system. Based on higher order spectral analysis, different signal processing techniques are developed to assess health conditions of different critical rotating-components in the AH-64D helicopter drive-train. Based on cross-bispectrum, quadratic nonlinear transfer function is presented to model second order nonlinearity in a drive-shaft running between the two hanger bearings. Then, quadratic-nonlinearity coupling coefficient between frequency harmonics of the rotating shaft is used as condition metric to study different seeded shaft faults compared to baseline case, namely: shaft misalignment, shaft imbalance, and combination of shaft misalignment and imbalance. The proposed quadratic-nonlinearity metric shows better capabilities in distinguishing the four studied shaft settings than the conventional linear coupling based on cross-power spectrum. We also develop a new concept of Quadratic-Nonlinearity Power-Index spectrum, QNLPI(f), that can be used in signal detection and classification, based on bicoherence spectrum. The proposed QNLPI(f) is derived as a projection of the three-dimensional bicoherence spectrum into two-dimensional spectrum that quantitatively describes how much of the mean square power at certain frequency f is generated due to nonlinear quadratic interaction between different frequency components. The proposed index, QNLPI(f), can be used to simplify the study of bispectrum and bicoherence signal spectra. It also inherits useful characteristics from the bicoherence such as high immunity to additive Gaussian noise, high capability of nonlinear-systems identifications, and amplification invariance. The quadratic-nonlinear power spectral density PQNL(f) and percentage of quadratic nonlinear power PQNLP are also introduced based on the QNLPI(f). Concept of the proposed indices and their computational considerations are discussed first using computer generated data, and then applied to real-world vibration data to assess health conditions of different rotating components in the drive train including drive-shaft, gearbox, and hanger bearing faults. The QNLPI(f) spectrum enables us to gain more details about nonlinear harmonic generation patterns that can be used to distinguish between different cases of mechanical faults, which in turn helps to gaining more diagnostic/prognostic capabilities.
机译：为了有效地维护各种飞机和旋翼飞机，必须基于旋转部件监控的振动信号建立有效的基于状态的维护（CBM）。本文介绍了多光谱信号处理技术在AH-64D直升机机尾旋翼传动系统关键部件状态监测中的理论与应用。当前可用的振动监测工具主要围绕自动和互功率谱分析构建，在检测高于二阶的频率相关性方面性能有限。研究高阶相关性及其傅立叶变换，高阶谱可提供有关振动信号的更多信息，这有助于建立更准确的机械系统诊断模型。基于高阶频谱分析，开发了不同的信号处理技术来评估AH-64D直升机传动系统中不同关键旋转组件的健康状况。基于交叉双谱，提出了二次非线性传递函数来模拟两个吊架轴承之间驱动轴的二阶非线性。然后，将旋转轴的频率谐波之间的二次非线性耦合系数用作条件度量，以研究与基准情况相比不同的种子轴故障，即：轴未对准，轴不平衡以及轴未对准和不平衡的组合。与基于交叉功率谱的常规线性耦合相比，所提出的二次非线性度量在区分四种研究的轴设置方面显示出更好的功能。我们还开发了二次非线性功率指数频谱QNLPI（f）的新概念，该概念可用于基于双相干频谱的信号检测和分类。提出的QNLPI（f）是三维双相干频谱到二维频谱的投影，该频谱定量描述了由于不同频率分量之间的非线性二次相互作用而在一定频率f下产生了多少均方功率。拟议的指标QNLPI（f）可用于简化双谱和双相干信号谱的研究。它还具有双相干性的有用特性，例如对加性高斯噪声的高度抗扰性，对非线性系统的识别能力强以及放大不变性。还基于QNLPI（f）介绍了二次非线性功率谱密度PQNL（f）和二次非线性功率PQNLP的百分比。首先使用计算机生成的数据讨论所建议指标的概念及其计算注意事项，然后将其应用于实际振动数据以评估传动系统中不同旋转部件（包括传动轴，齿轮箱和吊架轴承故障）的健康状况。 QNLPI（f）频谱使我们能够获得有关非线性谐波生成模式的更多详细信息，这些模式可用于区分机械故障的不同情况，进而有助于获得更多的诊断/诊断能力。

著录项

  • 作者

    Hassan Mohammed, Mohammed Ahmed.;

  • 作者单位

    University of South Carolina.;

  • 授予单位 University of South Carolina.;
  • 学科 Engineering Electronics and Electrical.;Engineering Aerospace.
  • 学位 Ph.D.
  • 年度 2013
  • 页码 117 p.
  • 总页数 117
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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