An air turbine starter (ATS) is used to start the aero-engine before an aircraft takes off, which plays a significant role in the reliable operation of the aero-engine and is critical to the flight security, so it is vital to monitor the health and predict the remaining useful life (RUL) for the ATS. This paper proposes a fusion framework based on the combination of empirical mode decomposition (EMD) and relevance vector machine (RVM). EMD is used to smooth out the non-stationary data by pattern decomposition, and the multiple intrinsic mode functions (IMF) which can effectively reflect the fault characteristics, are carefully selected from all IMFs by kurtosis index technique. RVM is used to train the selected smooth IMFs samples and establish a regression model for remaining useful life prediction. In addition, the subtraction clustering technique is introduced to reduce the samples scale and speed up the RVM’s training efficiency. The effectiveness of the proposed fusion framework is illustrated via an experiment of ATS, and the results show that the proposed method has satisfactory prediction performance.

中文翻译：

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基于经验模态分解和相关向量机的空气涡轮起动机剩余寿命预测

空气涡轮启动器（Air Turbo Starter，ATS）用于在飞机起飞前启动航空发动机，对航空发动机的可靠运行起着重要作用，对飞行安全至关重要，因此对航空发动机进行监控至关重要。健康并预测 ATS 的剩余使用寿命 (RUL)。本文提出了一种基于经验模式分解（EMD）和相关向量机（RVM）相结合的融合框架。EMD用于通过模式分解对非平稳数据进行平滑处理，并通过峰态指数技术从所有IMF中精心挑选出能够有效反映故障特征的多个固有模式函数（IMF）。RVM 用于训练选定的平滑 IMFs 样本并建立回归模型以进行剩余使用寿命预测。此外，引入减法聚类技术以减小样本规模并加快RVM的训练效率。通过ATS的实验说明了所提出的融合框架的有效性，结果表明所提出的方法具有令人满意的预测性能。