Wear degree detection of gears is an effective way to prevent faults. However, due to the interference of high-speed meshing vibration and environmental noise, the weak vibration signal generated by the gear is easily covered by the noise, which makes it difficult to detect the degree of wear. To address this issue, this paper proposes a novel gear wear degree diagnosis method based on local weighted scatter smoothing method (LOWESS), wavelet packet transform (WPT), and least square support vector machine (APSO-LSSVM) optimized by adaptive particle swarm algorithm. According to the low signal-to-noise ratio characteristic of gear vibration signal, LOWESS is first used to preprocess the signal spectrum. Then, the characteristic parameters used to characterize gear wear are extracted from different decomposition depths by WPT and, finally, combined with APSO-SVM to diagnose the degree of gear wear. Compared with the basic least squares support vector machine, the improved method has better performance in sample classification. The experimental results show that the method in this paper can effectively reduce the diagnosis error caused by background noise, and the diagnosis accuracy reaches 98.33%, which can provide a solution for the health status monitoring of gears.

中文翻译：

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用于齿轮磨损度诊断的小波包变换辅助最小二乘支持向量机

齿轮磨损程度检测是预防故障的有效方法。但由于高速啮合振动和环境噪声的干扰，齿轮产生的微弱振动信号很容易被噪声所掩盖，从而难以检测磨损程度。针对这一问题，本文提出了一种基于局部加权散射平滑法（LOWESS）、小波包变换（WPT）和自适应粒子群算法优化的最小二乘支持向量机（APSO-LSSVM）的新型齿轮磨损度诊断方法。 . 根据齿轮振动信号的低信噪比特性，首先采用LOWESS对信号频谱进行预处理。然后，通过 WPT 从不同的分解深度中提取用于表征齿轮磨损的特征参数，最后，结合APSO-SVM诊断齿轮磨损程度。与基本最小二乘支持向量机相比，改进后的方法在样本分类方面具有更好的性能。实验结果表明，本文方法能有效降低背景噪声引起的诊断误差，诊断准确率达到98.33%，为齿轮健康状态监测提供了一种解决方案。