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A gearbox fault diagnosis method based on MKurt spectrum and CYCBD

机译：基于MKURT谱和CYCBD的变速箱故障诊断方法

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摘要

Maximum second-order cyclostationary blind deconvolution (CYCBD) is a good signal denoising method, which can be employed for gear fault diagnosis. However, CYCBD is highly dependent on the pre-period of the measured signal, which needs to be appropriately predetermined. To address this issue, a new method based on the multi-point kurtosis (MKurt)spectrum and CYCBD is proposed, which can be used for extracting fault features in a gearbox. First, deconvolution period T, the key parameter of CYCBD, is accurately selected using the MKurt spectrum. Then, according to the selected key parameter, CYCBD is employed to process the gearbox signals and identify the fault type. This proposed method is applied to the analysis of single and compound fault signals of a gearbox; the large gear and pinion fault signals under strong background noise are separated. Finally, the fault signals obtained by CYCBD are analysed using an envelope spectrum to extract the fault features. The two case studies demonstrate that the proposed method can effectively identify the gear faults. Moreover, the results show the superior effectiveness and reliability of this proposed method compared with the maximum correlation kurtosis deconvolution (MCKD) method.
机译：最大二阶睫状裂纹盲（Cycbd）是一种良好的信号去噪方法，可用于齿轮故障诊断。然而，Cycbd高度依赖于测量信号的前一段，这需要适当地预定。为了解决这个问题，提出了一种基于多点峰度（MKURT）光谱和CYCBD的新方法，可用于提取变速箱中的故障特征。首先，使用MKURT光谱准确选择Cycbd的关键参数Deconvolution时段T.然后，根据所选择的密钥参数，采用Cycbd来处理变速箱信号并识别故障类型。该提出的方法应用于齿轮箱的单一和复合故障信号的分析;在强大的背景噪声下，大型齿轮和小齿轮故障信号分离。最后，使用信封频谱分析Cycbd获得的故障信号，以提取故障特征。这两种案例研究表明，该方法可以有效地识别齿轮断层。此外，结果显示了与最大相关峰衰减（McKD）方法相比的这种方法的优异效率和可靠性。

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