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Development of a new signal processing diagnostic tool for vibration signals acquired in transient conditions

机译：开发用于瞬态条件下获取的振动信号的新型信号处理诊断工具

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摘要

The diagnostics of mechanical components operating in transient conditions is still an open issue, in both research and industrial field. Indeed, the signal processing techniques developed to analyse stationary data are not applicable or are affected by a loss of effectiveness when applied to signal acquired in transient conditions. In this paper, a suitable and original signal processing tool (named EEMED), which can be used for mechanical component diagnostics in whatever operating condition and noise level, is developed exploiting some data-adaptive techniques such as Empirical Mode Decomposition (EMD), Minimum Entropy Deconvolution (MED) and the analytical approach of the Hilbert transform. The proposed tool is able to supply diagnostic information on the basis of experimental vibrations measured in transient conditions. The tool has been originally developed in order to detect localized faults on bearings installed in high speed train traction equipments and it is more effective to detect a fault in non-stationary conditions than signal processing tools based on spectral kurtosis or envelope analysis, which represent until now the landmark for bearings diagnostics.
机译：在研究和工业领域中，瞬态条件下运行的机械部件的诊断仍然是一个未解决的问题。实际上，开发用于分析静态数据的信号处理技术不适用，或者在应用于瞬态条件下获取的信号时会受到有效性损失的影响。在本文中，开发了一种合适的原始信号处理工具（名为EEMED），该工具可以利用一些数据自适应技术（例如经验模态分解（EMD），最小化）用于任何工作条件和噪声水平下的机械组件诊断。熵反卷积（MED）和希尔伯特变换的解析方法。所提出的工具能够根据在瞬态条件下测得的实验振动提供诊断信息。该工具最初是为了检测安装在高速火车牵引设备上的轴承上的局部故障而开发的，它比基于频谱峰度或包络分析的信号处理工具更有效地检测非平稳状态下的故障，直到现在为止现在是轴承诊断的里程碑。

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