Fault warning method for aircraft engine rolling bearings based on characteristic energy

被引：0

作者：

Luan, Xiaochi ^{[1
]}

Zhao, Junhao ^{[1
]}

Sha, Yundong ^{[1
]}

Liu, Xinhang ^{[1
]}

Zhang, Wenhao ^{[1
]}

Yang, Jie ^{[2
]}

机构：

[1] Liaoning Key Lab of Advanced Test Technology for Aerospace Propulsion System, School of Aero-engine, Shenyang Aerospace University, Shenyang

[2] Shenyang Engine Research Institute, Aero Engine Corporation of China, Shenyang

来源：

Hangkong Dongli Xuebao/Journal of Aerospace Power | 2025年 / 40卷 / 07期

关键词：

characteristic energy; fault diagnosis; fault warning; kurtosis-correlation coefficient screening criteria; rolling bearing;

D O I：

10.13224/j.cnki.jasp.20240094

中图分类号：

学科分类号：

摘要：

A method for real-time monitoring of rolling bearings in aircraft engines based on characteristic energy was proposed to address the challenging issue of real-time monitoring of rolling bearings in aircraft engines. This method first decomposed the original vibration signal using CEEMDAN to obtain several components, and then calculated the kurtosis and correlation coefficient of each component. Subsequently, based on the kurtosis-correlation coefficient criterion, it selected strong impact components for reconstruction and performed envelope demodulation to maximally retain effective information related to bearing fault impact components. Finally, the method calculated the characteristic energy of faulty and normal bearings from the information in the envelope spectrum, established a diagnostic baseline and characteristic energy belt, and achieved monitoring of bearing operating status. The effectiveness of this approach was validated using data from the Case Western Reserve University deep groove ball bearing test rig, a constructed rolling bearing test rig, and a test rig for a certain type of turbofan aircraft engine bearing component. The results showed that the proportion of the characteristic energy of the outer ring fault bearing in the whole envelope spectrum energy was 59.5%—75.9%, and the method can provide an effective means for the fault diagnosis and online monitoring of the main bearing of aircraft engine. © 2025 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

引用

共 22 条

[1]

WEI Xunkai, FENG Yue, YANG Li, Et al., Research on fault prediction of aero-engine intermediate main bearing, Beijing: Aviation Safety and Equipment Maintenance Technology: the Symposium on Aviation Safety and Equipment Maintenance Technology, (2014)

[2]

JIA Yanqiu, ZHANG Bing, CHEN Xuemei, Malfunction mechanism and diagnosis of rolling bearing, Chemical Equipment Technology, 32, 4, pp. 55-57, (2011)

[3]

OH J W，, PARK D，, JEONG J., Fault detection for lubricant bearing with CNN, 2019 2nd International Conference on Intelligent Autonomous Systems, pp. 142-145, (2019)

[4]

Zhigang LIU, SUN Wanlu, ZENG Jiajun, A new short-term load forecasting method of power system based on EEMD and SS-PSO [J], Neural Computing and Applications, 24, 3, pp. 973-983, (2014)

[5]

LI Changlin, KONG Fanrang, HUANG Weiguo, Et al., Rolling bearing fault diagnosis based on EEMD and Laplace wavelet, Journal of Vibration and Shock, 33, 3, pp. 63-69, (2014)

[6]

TORRES M E，, COLOMINAS M A, Et al., A complete ensemble empirical mode decomposition with adaptive noise, 2011 IEEE International Conference on Acoustics，Speech and Signal Processing, pp. 4144-4147, (2011)

[7]

Zhiqiang CHEN, Liang GUO, Hongli GAO, Et al., A fault pulse extraction and feature enhancement method for bearing fault diagnosis[J], Measurement, 182, (2021)

[8]

BOUHALAIS M L，, DJEBALA A，, OUELAA N，, Et al., CEEMDAN and OWMRA as a hybrid method for rolling bearing fault diagnosis under variable speed, The International Journal of Advanced Manufacturing Technology, 94, 5, pp. 2475-2489, (2018)

[9]

ZHANG Xiaoyuan, Yitao LIANG, ZHOU Jianzhong, Et al., A novel bearing fault diagnosis model integrated permutation entropy，ensemble empirical mode decomposition and optimized SVM[J], Measurement, 69, pp. 164-179, (2015)

[10]

SHI Zongli, SONG Wanqing, TAHERI S., Improved LMD，permutation entropy and optimized K-means to fault diagnosis for roller bearings, Entropy, 18, 3, (2016)

← 1 2 3 →