The difficulty of knowing the real-time status of gas face seals is the main cause of common problems, including sudden failure, ineffective diagnosis, and unpredictability of service life. This study analyzed the acoustic emission signals generated from experiments, uncovering their features in terms of the frequency distribution, periodic fluctuations, and the behaviors during different operation phases. A new vectorization procedure was designed according to the knowledge of informative acoustic emission features. Based on the vectorization procedure, a support vector machine regression method was applied to develop models predicting the eccentric load on the stator of the seal. Cross-validation was conducted to evaluate the regression performance and search for a proper kernel scale. This study found the informative features of acoustic emissions at different timescales and during different seal operation phases, and particularly the great informative potential of certain segments of the starting and stopping phases. The vectorization and support vector machine regression were shown to be effective in estimating the loads in experiments with cross-validation. Thus, a method for estimating the status of gas face seals based on acoustic emission monitoring was established.

难以得知气面密封件的实时状态是常见问题的主要原因，这些常见问题包括突然失效，诊断无效以及使用寿命的不可预测性。这项研究分析了实验产生的声发射信号，揭示了它们在频率分布，周期性波动以及不同操作阶段的行为方面的特征。根据丰富的声发射特征知识，设计了一种新的矢量化程序。基于矢量化过程，使用支持向量机回归方法来开发模型，以预测密封件定子上的偏心载荷。进行交叉验证以评估回归性能并寻找合适的内核规模。这项研究发现了在不同的时间尺度上和在不同的密封操作阶段，声发射的信息特征，尤其是在开始和停止阶段的某些部分具有巨大的信息潜力。在交叉验证实验中，矢量化和支持向量机回归被证明可有效地估计负荷。因此，建立了一种基于声发射监测的气面密封状态估计方法。