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Low-rank enhanced convolutional sparse feature detection for accurate diagnosis of gearbox faults
Mechanical Systems and Signal Processing ( IF 7.9 ) Pub Date : 2021-03-01 , DOI: 10.1016/j.ymssp.2020.107215
Zhaohui Du , Xuefeng Chen , Han Zhang , Yixin Yang

Abstract It is a challenge problem to accurately recognize damage distribution pattern for multi-stage industrial gearboxes in filed, due to entangled relationships between strong interferences/noises 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.

中文翻译:

用于齿轮箱故障准确诊断的低秩增强卷积稀疏特征检测

摘要 由于强干扰/噪声和复杂的传输路径调制之间的纠缠关系,在现场准确识别多级工业齿轮箱的损伤分布模式是一个挑战。在这项工作中,提出了一种基于低秩表示和卷积稀疏学习的定制两阶段策略(LR-CSL)。基于聚焦特征的周期性相似性,首先利用加权低秩阶段来抑制强干扰和噪声,这为增强盲反卷积方法提供了基石。然后,采用卷积稀疏阶段通过强制执行一个非负有界正则化器来减轻传输路径调制,从而保证脉冲源包络的可靠恢复。最后,通过直接参考恢复的源包络(而不是调制波形)和齿轮箱动力学,可以可靠地确认损坏分布模式。对一个 750 kW 风力涡轮机动力传动系统进行全面的健康评估,并准确识别具有多个弱剥落模式的齿轮表面。此外,推导了剥落故障的演变过程并分配了维护指导。进一步的分析还证实,第一个低阶阶段在提高 LR-CSL 的反卷积能力方面起着必要和重要的作用。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准确性。对一个 750 kW 风力涡轮机动力传动系统进行全面的健康评估,并准确识别具有多个弱剥落模式的齿轮表面。此外,推导了剥落故障的演变过程并分配了维护指导。进一步的分析还证实,第一个低阶阶段在提高 LR-CSL 的反卷积能力方面起着必要和重要的作用。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准确性。对一个 750 kW 风力涡轮机动力传动系统进行全面的健康评估,并准确识别具有多个弱剥落模式的齿轮表面。此外,推导了剥落故障的演变过程并分配了维护指导。进一步的分析还证实,第一个低阶阶段在提高 LR-CSL 的反卷积能力方面起着必要和重要的作用。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准确性。进一步的分析还证实,第一个低阶阶段在提高 LR-CSL 的反卷积能力方面起着必要和重要的作用。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准确性。进一步的分析还证实,第一个低阶阶段在提高 LR-CSL 的反卷积能力方面起着必要和重要的作用。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准确性。
更新日期:2021-03-01
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