As a crucial component supporting aero-engine functionality, effective fault diagnosis of bearings is essential to ensure the engine's reliability and sustained airworthiness. However, practical limitations prevail due to the scarcity of aero-engine bearing fault data, hampering the implementation of intelligent diagnosis techniques. This paper presents a specialized method for aero-engine bearing fault diagnosis under conditions of limited sample availability. Initially, the proposed method employs the refined composite multiscale phase entropy (RCMPhE) to extract entropy features capable of characterizing the transient signal dynamics of aero-engine bearings. Based on the signal amplitude information, the composite multiscale decomposition sequence is formulated, followed by the creation of scatter diagrams for each sub-sequence. These diagrams are partitioned into segments, enabling individualized probability distribution computation within each sector, culminating in refined entropy value operations. Thus, the RCMPhE addresses issues prevalent in existing entropy theories such as deviation and instability. Subsequently, the bonobo optimization support vector machine is introduced to establish a mapping correlation between entropy domain features and fault types, enhancing its fault identification capabilities in aero-engine bearings. Experimental validation conducted on drivetrain system bearing data, actual aero-engine bearing data, and actual aerospace bearing data demonstrate remarkable fault diagnosis accuracy rates of 99.83%, 100%, and 100%, respectively, with merely 5 training samples per state. Additionally, when compared to the existing eight fault diagnosis methods, the proposed method demonstrates an enhanced recognition accuracy by up to 28.97%. This substantiates its effectiveness and potential in addressing small sample limitations in aero-engine bearing fault diagnosis.

中文翻译：

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航空发动机轴承有限样本高精度智能故障诊断方法

作为支撑航空发动机功能的关键部件，轴承的有效故障诊断对于确保发动机的可靠性和持续适航至关重要。然而，由于航空发动机轴承故障数据的缺乏，实际应用中存在局限性，阻碍了智能诊断技术的实施。本文提出了一种在样本有限的情况下进行航空发动机轴承故障诊断的专用方法。最初，该方法采用精细复合多尺度相位熵（RCMPhE）来提取能够表征航空发动机轴承瞬态信号动态的熵特征。基于信号幅度信息，制定复合多尺度分解序列，然后为每个子序列创建散点图。这些图被划分为多个部分，从而能够在每个部分内进行个性化的概率分布计算，最终实现精细的熵值运算。因此，RCMPhE 解决了现有熵理论中普遍存在的问题，例如偏差和不稳定性。随后，引入Bonobo优化支持向量机建立熵域特征与故障类型之间的映射关系，增强其在航空发动机轴承中的故障识别能力。对传动系统轴承数据、实际航空发动机轴承数据和实际航空航天轴承数据进行的实验验证表明，每个状态仅需要 5 个训练样本，故障诊断准确率分别达到 99.83%、100% 和 100%。此外，与现有的八种故障诊断方法相比，该方法的识别准确率提高了高达28.97%。这证实了其在解决航空发动机轴承故障诊断中小样本限制方面的有效性和潜力。