典型包络解调方法在空间执行机构轴承中的诊断研究

高 超1, 乔 凯1, 郝 晓龙1, 周 刚2, 王 剑文3
1、北京跟踪与通信技术研究所
2、北京控制工程研究所
3、北京航空航天大学交通科学与工程学院

摘要


轴承是控制力矩陀螺、飞轮等空间惯性执行机构的核心部件,其健康状态直接影响着整机性能和使用寿命。
由于其性能退化机理尚不完全清晰,一些故障诊断方法在实际应用中常出现虚警、漏警及误诊等情况。针对这一问
题,本文提出了一套轴承故障诊断算法有效性评估流程,通过多种评价指标从多个角度揭示算法诊断能力。然后以
此为基础,对比了不同诊断算法对地面承载轴承和空间轴承诊断的有效性与准确性,为空间机构轴承诊断算法的改
进指明方向。最后,构建基于包络解调的轴承故障诊断方法,提高了故障诊断的有效性。

关键词


空间轴承;算法评估;故障诊断;包络分析;状态评估

全文:

PDF


参考


[1]Islam M. S., Rahimi A. Use of a data-driven approach

for time series prediction in fault prognosis of satellite reaction

wheel [C]. Proceedings of the IEEE International Conference

on Systems, Man, and Cybernetics (SMC), 2020.[2]Chen G. Y., Lu G. L., Liu J., et al. An integrated

framework for statistical change detection in running status of

industrial machinery under transient conditions [J]. ISA Trans,

2019, 94: 294-306.

[3]Xu L., Chatterton S., Pennacchi P. Rolling element

bearing diagnosis based on singular value decomposition and

composite squared envelope spectrum [J]. Mechanical Systems

and Signal Processing, 2021, 148: 25.

[4]Zhang T. C., Chen J. L., Li F. D., et al. Intelligent

fault diagnosis of machines with small & imbalanced data:

A state-of-the-art review and possible extensions [J]. ISA

Trans, 2022, 119: 152-71.

[5]Dzakow I. Je.,Valentine G. S. Advanced techniques

for the verification and validation of prognostics & health

management capabilities [A]. Proceedings of the 60th

Meeting of the Society for Machinery Failure Prevention

Technology,Vinginia a Beach Vinginia,2006.

[6]Hess A., Calvello G., Dabney T. PHM a key enabler

for the JSF autonomic logistics support concept [C]. IEEE

Aerospace Conference, 2004.

[7]Massam S., McMullan. Verification of PHM

capabilities: a joint customer/industrial perspective [C].

Proceedings of the 2002 IEEE Aerospace Conference, 2002.

[8]Saxena A., Celaya J., Saha B., et al. Metrics for offline

evaluation of prognostic performance [C]. International

Journal of Prognostic and Health Management, 2010.

[9]隆金波,曲昌琦,蒋觉义等.典型机电系统PHM

评价指标分析[J].计算机测量与控制,2021,29(06):

255-259.

[10]Chen X. Y., Ge D., Liu X., et al. Roller bearing

fault diagnosis based on empirical mode decomposition

and targeting feature selection [C]. Proceedings of the 3rd

International Conference on Information Processing and

Control Engineering, Moscow, 2019.

[11]Han M. H., Wu Y. M., Wang Y. M., et al.

Roller bearing fault diagnosis based on LMD and multiscale symbolic dynamic information entropy [J]. J Mech Sci

Technol, 2021, 35(5): 1993-2005.

[12]Case Western Reserve University nearing

center. Bearing data [EB/OL]. http://csegroups.case.edu/

bearingdatacenter/home.

[13]梅宏斌.滚动轴承振动监测与诊断[M].北京:机

械工业出版社,1995:38-39.

[14]Wu D. Y., Wang J. W., Wang H., et al. An

automatic bearing fault diagnosis method based on

characteristics frequency ratio [J]. Sensors, 2020, 20(5): 12.


Refbacks

  • 当前没有refback。