国外文献 > 1.轴承 > 7. 智能方法与计算 > 神经网络
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| 标 题： |
A high-accuracy intelligent fault diagnosis method for aero-engine bearings with limited samples
基于有限样本的航空发动机轴承高精度智能故障诊断方法 |
| 作 者： |
Wang, Zhenya
Luo, Qiusheng
Chen, Hui
Zhao, Jingshan
Yao, Ligang
Zhang, Jun
Chu, Fulei |
| 关键词： |
Aero-engine bearing [航空发动机轴承]
Fault diagnosis [ 故障诊断]
Multiscale phase entropy [ 多尺度相熵]
Bonobo optimizer [ Bonobo 优化器]
Support vector machine [ 支持向量机]
MACHINE [ 机器]
ENTROPY [ 熵]
SCHEME [ 方案] |
| 出版年： |
2024 |
| 期刊名： |
Computers in Industry
工业计算机 |
| 年卷期： |
2024, vol.159/160 |
| 摘 要： |
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.
作为支持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与持续适航性至关重要。然而，由于航空发动机轴承故障数据匮乏，实际应用中存在诸多限制，制约了智能诊断技术的实施。本文提出了一种在样本有限条件下用于航空发动机轴承故障诊断的专用方法。首先，该方法采用改进型复合多尺度相位熵（RCMPhE），提取能够表征航空发动机轴承瞬态信号动态特性的熵特征。基于信号振幅信息，构建复合多尺度分解序列，并对每个子序列生成散点图。随后将这些散点图划分为若干段，实现各区域内的概率分布计算，最终完成精细化的熵值运算。因此，RCMPhE有效解决了现有熵理论中存在的偏差和不稳定性等问题。随后，引入了黑猩猩优化支持向量机，用于建立熵域特征与故障类型之间的映射关系，从而提升其在航空发动机轴承故障识别中的能力。对传动系统轴承数据、实际航空发动机轴承数据以及真实航空航天轴承数据进行的实验验证表明，该方法分别实现了99.83%、100%和100%的显著故障诊断准确率，且每个状态仅需5个训练样本。此外，与现有的八种故障诊断方法相比，所提出的方法识别准确率最高可提升28.97%。这充分证明了该方法在解决航空发动机轴承故障诊断中小样本问题方面的有效性与潜力。 |
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