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Tribological behaviour diagnostic and fault detection of mechanical seals based on acoustic emission measurements

机译：基于声发射测量的机械密封的摩擦学行为诊断和故障检测

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摘要

Acoustic emission (AE) has been studied for monitoring the condition of mechanical seals by many researchers, however to the best knowledge of the authors, typical fault cases and their effects on tribological behaviour of mechanical seals have not yet been successfully investigated. In this paper, AE signatures from common faults of mechanical seals are studied in association with tribological behaviour of sealing gap to develop more reliable condition monitoring approaches. A purpose-built test rig was employed for recording AE signals from the mechanical seals under healthy and faulty conditions. The collected data was then processed using time domain and frequency domain analysis methods. The study has shown that AE signal parameters: root mean squared (RMS) along with AE spectrum, allows fault conditions including dry running, spring out and defective seal faces to be diagnosed under a wide range of operating conditions. However, when mechanical seals operate around their transition point, conventional signal processing methods may not allow a clear separation of the fault conditions from the healthy baseline. Therefore an auto-regressive (AR) model has been developed on recorded AE signals to classify different fault conditions of mechanical seals and satisfactory results have been perceived.
机译：许多研究人员已经研究了声发射（AE）来监测机械密封的状况，但是据作者所知，尚未成功研究典型的故障案例及其对机械密封的摩擦学行为的影响。在本文中，结合密封间隙的摩擦学特性，研究了机械密封常见故障的AE特征，以开发出更可靠的状态监测方法。在健康和故障情况下，使用专用测试设备记录来自机械密封的AE信号。然后使用时域和频域分析方法处理收集的数据。研究表明，AE信号参数：均方根（RMS）以及AE频谱，可以在各种工作条件下诊断故障条件，包括空转，弹跳和密封面缺陷。但是，当机械密封件在其过渡点附近工作时，传统的信号处理方法可能无法将故障情况与正常基线明确分开。因此，已经在记录的AE信号上建立了自回归（AR）模型，以对机械密封的不同故障条件进行分类，并获得了令人满意的结果。

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