Purpose

Gearboxes are important for the mechanical power transmission in rotary systems. Gearbox failure may lead to an increase in downtime and production loss. Hence, effective and reliable working gearboxes are needed for regular health monitoring and controlling of the excessive vibration of the system. The purpose of this work is to use a vibration-based technique to automate the bevel gear wear fault diagnosis. It is thus expected that our novel systematic and procedural analysis would help to accurately identify multi-class gearbox faults.

Methods

In this study, a flexible analytic wavelet transform method was used to decompose the bevel gear wear signal into sub-band signals. Various entropies, such as cross-correntropy, log energy entropy, Stein’s unbiased risk estimate entropy, Shannon entropy, norm entropy, and threshold entropy were used for feature extraction from all of the sub-band signals. The Kruskal–Wallis test was also used to obtain statistically meaningful results. Subsequently, these quantitative features were fed to the Least-Squares Support Vector Machine (LS-SVM) classifier.

Results and Conclusions

These methodologies are found to produce the most accurate results by using the log energy entropy-based multi-class LS-SVM classifier and the RBF kernel function. The results obtained here are compared with the previous results obtained by different methods, such as the continuous wavelet transform (CWT), discrete wavelet transform (DWT), wavelet packet transform (WPT), dual-tree complex wavelet transform (DTCWT), and tunable-Q wavelet transform (TQWT).

中文翻译：

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在柔性分析小波变换（FAWT）域中使用基于熵的特征进行自动变速箱故障诊断

目的

变速箱对于旋转系统中的机械动力传输很重要。变速箱故障可能导致停机时间增加和生产损失。因此，需要有效而可靠的工作齿轮箱来进行定期的健康监测和系统过度振动的控制。这项工作的目的是使用基于振动的技术来自动进行锥齿轮磨损故障诊断。因此，可以预期，我们新颖的系统和程序分析将有助于准确地识别多类变速箱故障。

方法

在这项研究中，使用灵活的解析小波变换方法将锥齿轮磨损信号分解为子带信号。从所有子带信号中提取各种熵，例如交叉熵，对数能量熵，Stein无偏风险估计熵，Shannon熵，范数熵和阈值熵。Kruskal–Wallis检验还用于获得统计上有意义的结果。随后，这些定量特征被输入到最小二乘支持向量机（LS-SVM）分类器。

结果与结论

通过使用基于对数能量熵的多类LS-SVM分类器和RBF核函数，发现这些方法可产生最准确的结果。将此处获得的结果与通过不同方法获得的先前结果进行比较，例如连续小波变换（CWT），离散小波变换（DWT），小波包变换（WPT），双树复小波变换（DTCWT）和可调Q小波变换（TQWT）。