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Vibration analysis of rotating machinery using time-frequency analysis and wavelet techniques

机译：利用时频分析和小波技术的旋转机械振动分析

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Time-frequency analysis, including the wavelet transform, is one of the new and powerful tools in the important field of structural health monitoring, using vibration analysis. Commonly-used signal analysis techniques, based on spectral approaches such as the fast Fourier transform, are powerful in diagnosing a variety of vibration-related problems in rotating machinery. Although these techniques provide powerful diagnostic tools in stationary conditions, they fail to do so in several practical cases involving non-stationary data, which could result either from fast operational conditions, such as the fast start-up of an electrical motor, or from the presence of a fault causing a discontinuity in the vibration signal being monitored. Although the short-time Fourier transform compensates well for the loss of time information incurred by the fast Fourier transform, it fails to successfully resolve fast-changing signals (such as transient signals) resulting from non-stationary environments. To mitigate this situation, wavelet transform tools are considered in this paper as they are superior to both the fast and short-time Fourier transforms in effectively analyzing non-stationary signals. These wavelet tools are applied here, with a suitable choice of a mother wavelet function, to a vibration monitoring system to accurately detect and localize faults occurring in this system. Two cases producing non-stationary signals are considered: stator-to-blade rubbing, and fast start-up and coast-down of a rotor. Two powerful wavelet techniques, namely the continuous wavelet and wavelet packet transforms, are used for the analysis of the monitored vibration signals. In addition, a novel algorithm is proposed and implemented here, which combines these two techniques and the idea of windowing a signal into a number of shaft revolutions to localize faults.
机译：包括小波变换在内的时频分析是使用振动分析在结构健康监测的重要领域中的一种新的强大工具。基于频谱方法（例如快速傅立叶变换）的常用信号分析技术在诊断旋转机械中与振动相关的各种问题方面非常有力。尽管这些技术在固定条件下提供了强大的诊断工具，但在涉及非固定数据的几种实际情况下却无法做到这一点，这可能是由于快速运行条件（例如电动机的快速启动）或电动机出现故障，导致所监测的振动信号不连续。尽管短时傅立叶变换很好地弥补了快速傅立叶变换所造成的时间信息的损失，但它无法成功解析非平稳环境导致的快速变化的信号（例如瞬态信号）。为了缓解这种情况，本文考虑使用小波变换工具，因为它们在有效分析非平稳信号方面优于快速和短时傅立叶变换。这些小波工具在适当选择母子波功能的情况下，被应用于振动监测系统，以准确地检测和定位该系统中发生的故障。考虑了产生非平稳信号的两种情况：定子到叶片的摩擦，以及转子的快速启动和滑行。两种强大的小波技术，即连续小波和小波包变换，用于分析所监测的振动信号。另外，这里提出并实现了一种新颖的算法，该算法结合了这两种技术以及将信号开窗到许多轴旋转中以定位故障的思想。

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