As a part of the energy transmission chain, gearboxes are considered as important components in rotating machines, and the gearbox failure results in costly economic losses. Therefore, it is necessary to detect the appearance of incipient gearbox faults by implementing an appropriate detected model. The incipient failure characteristics of the gearbox are weak and hidden in a set of time-varying series signals the vibration signals, which is difficult to effectively extract under the background of strong noise. The PCA method is not effective in detecting weak fault features in time-varying signals, so this paper proposes a method based on Deep Recursive Dynamic Principal Component Analysis (Deep RDPCA) to detect incipient faults in gearboxes. The proposed approach is modeled via both the deep decomposed theorems and time-varying dynamic model based on traditional PCA to extract characteristic of time-varying and weak fault information under the background of strong noise. The proposed method could get a better real-time reflection for changed system by introducing “Moving Window” technologies, so that the incipient fault of gearbox could be detected accurately, too. Finally, the effect of Deep RDPCA-based fault diagnosis is compared with the results of PCA, DPCA, RDPCA, Deep PCA, and Deep DPCA methods. It is concluded that the proposed method can effectively capture the time-varying relationship of process variables and accurately extract the weak fault characteristics in the vibration signal, which effectively improves the fault detection performance.

中文翻译：

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基于深度递归动态主成分分析的齿轮箱早期故障检测


齿轮箱作为能量传输链的一部分，被认为是旋转机械的重要部件，齿轮箱的故障会导致高昂的经济损失。因此，有必要通过实施适当的检测模型来检测变速箱早期故障的出现。齿轮箱的早期故障特征较弱，隐藏在一组时变序列信号振动信号中，在强噪声背景下难以有效提取。 PCA方法不能有效地检测时变信号中的微弱故障特征，因此本文提出了一种基于深度递归动态主成分分析（Deep RDPCA）的方法来检测齿轮箱的早期故障。该方法通过深度分解定理和基于传统PCA的时变动态模型进行建模，以提取强噪声背景下的时变特征和微弱故障信息。该方法通过引入“移动窗口”技术，可以更好地实时反映变化的系统，从而准确检测齿轮箱的早期故障。最后，将基于Deep RDPCA的故障诊断效果与PCA、DPCA、RDPCA、Deep PCA、Deep DPCA方法的结果进行了比较。结果表明，该方法能够有效捕捉过程变量的时变关系，准确提取振动信号中的微弱故障特征，有效提高故障检测性能。