In predictive maintenance, vibration signal analyses are frequently used to diagnose reducer failures because these analyses contain information about the conditions of the mechanical components. Reducer vibration signals are very noisy and the signal-to-noise ratio is so low that extracting information from the signal components is complex, especially in practical situations. Therefore, signal processing techniques are used to solve this problem and facilitate the retrieval of information. In this work, the adopted technique included noise-canceling technique, synchronous temporal mean (TSA), and continuous Morlet wavelet transform (CWT), designed to extract resources and diagnose local gear damage. These techniques are used in measured signals in an experimental workbench consisting of the gear pair coupled to a motor and a generator. The experiment was monitored according to the conditions of a gear pair throughout its useful life. The continuous wavelet transforms accurately identified faults in the gear teeth, and it was possible to detect in which tooth the fault was occurring.

中文翻译：

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通过连续Morlet小波变换检测齿轮的点对点损坏

在预测性维护中，振动信号分析通常用于诊断减速器故障，因为这些分析包含有关机械组件状况的信息。减速器的振动信号非常嘈杂，信噪比非常低，以至于从信号分量中提取信息非常复杂，尤其是在实际情况下。因此，信号处理技术用于解决该问题并促进信息的检索。在这项工作中，采用的技术包括降噪技术，同步时间均值（TSA）和连续Morlet小波变换（CWT），旨在提取资源并诊断局部齿轮损坏。这些技术用于实验工作台中的测量信号，该工作台由耦合到电动机和发电机的齿轮对组成。在整个使用寿命期间，根据齿轮副的条件对实验进行监控。连续小波变换可以准确地识别出齿轮齿中的故障，并且可以检测出故障发生在哪个齿上。