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首页> 外文期刊>Mechanical systems and signal processing >Sparse signal decomposition method based on multi-scale chirplet and its application to the fault diagnosis of gearboxes
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Sparse signal decomposition method based on multi-scale chirplet and its application to the fault diagnosis of gearboxes

机译：基于多尺度chirplet的稀疏信号分解方法及其在齿轮箱故障诊断中的应用

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摘要

Based on the chirplet path pursuit and the sparse signal decomposition method, a new sparse signal decomposition method based on multi-scale chirplet is proposed and applied to the decomposition of vibration signals from gearboxes in fault diagnosis. An over-complete dictionary with multi-scale chirplets as its atoms is constructed using the method. Because of the multi-scale character, this method is superior to the traditional sparse signal decomposition method wherein only a single scale is adopted, and is more applicable to the decomposition of non-stationary signals with multi-components whose frequencies are time-varying. When there are faults in a gearbox, the vibration signals collected are usually AM-FM signals with multiple components whose frequencies vary with the rotational speed of the shaft. The meshing frequency and modulating frequency, which vary with time, can be derived by the proposed method and can be used in gearbox fault diagnosis under time-varying shaft-rotation speed conditions, where the traditional signal processing methods are always blocked. Both simulations and experiments validate the effectiveness of the proposed method.
机译：基于chirplet路径追踪和稀疏信号分解方法，提出了一种基于多尺度chirplet的稀疏信号分解方法，并将其应用于齿轮箱振动信号的分解，以进行故障诊断。使用该方法构建了以多尺度-为原子的超完备词典。由于具有多尺度特性，该方法优于仅采用单一尺度的传统稀疏信号分解方法，并且更适用于分解具有频率随时间变化的多分量的非平稳信号。当变速箱出现故障时，收集到的振动信号通常是具有多个分量的AM-FM信号，其频率随轴的转速而变化。随时间变化的啮合频率和调制频率可以通过所提出的方法来推导，并且可以用于在时变轴转速条件下变速箱的故障诊断，而在这种情况下，传统的信号处理方法总是受阻。仿真和实验均验证了该方法的有效性。

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