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首页> 外文期刊>Mechanical systems and signal processing >Low-rank enhanced convolutional sparse feature detection for accurate diagnosis of gearbox faults
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Low-rank enhanced convolutional sparse feature detection for accurate diagnosis of gearbox faults

机译：低级别增强型卷积稀疏特征检测，用于精确诊断变速箱故障

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摘要

It is a challenge problem to accurately recognize damage distribution pattern for multistage industrial gearboxes in filed, due to entangled relationships between strong interferencesoises and complicate transfer path modulations. In this work, a tailored two-stage strategy (LR-CSL) based on low-rank representation and convolutional sparse learning is proposed. Based on the periodic similarity of focused features, a weighted low-rank stage is firstly utilized to suppress strong interferences and noises, which provides a cornerstone to enhance blind deconvolution methods. Then, a convolutional sparse stage is adopted to mitigate the transfer path modulation by enforcing one nonnegative bounded regularizer, which guarantees the reliable recovery of impulsive source envelopes. Lastly, the damage distribution patterns could be reliably confirmed by directly referring to the recovered source envelopes (rather than modulated waveforms) and gearbox dynamics. Comprehensive health evaluations to one 750 kW wind turbine drivetrain are performed blindly and gear surfaces with multiple weak spalling patterns are recognized accurately. Moreover, the spalling fault evolution process is deduced and maintenance guidances are allocated. Further analysis also confirms the first low-rank stage plays a necessary and important role in boosting LR-CSL's deconvolution capability. Lastly, quantitative evaluations demonstrate that our LR-CSL method achieves a higher diagnostic accuracy than state-of-the-art fault diagnosis techniques.
机译：由于强大的干扰/噪声与复杂转移路径调制之间的缠结关系，是一种挑战问题，可以准确地识别备案中的多级工业齿轮箱的损伤分布模式。在这项工作中，提出了一种根据低秩表示和卷积稀疏学习的量身定制的两级策略（LR-CSL）。基于聚焦特征的周期性相似性，首先利用了加权低级阶段来抑制强烈的干扰和噪声，这提供了增强盲折叠方法的基石。然后，采用卷积稀疏阶段来通过实施一个非负界常规规范器来减轻传输路径调制，这保证了脉冲源信封的可靠恢复。最后，可以通过直接参考恢复的源包络（而不是调制波形）和变速箱动力学来可靠地确认损伤分布模式。盲目地进行到一个750 kW风力涡轮机传动系统的综合健康评估，并且准确地识别具有多种弱剥离图案的齿轮表面。此外，推断出剥落故障演化过程，并分配维护指导。进一步的分析还证实了第一级低级阶段在提高LR-CSL的折垃圾成果能力方面发挥必要和重要作用。最后，定量评估表明，我们的LR-CSL方法比最先进的故障诊断技术实现了更高的诊断精度。

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