航空发动机快变信号的匹配同步压缩变换研究

doi:10.3901/JME.2019.13.013

机械工程学报 ›› 2019, Vol. 55 ›› Issue (13): 13-22.doi: 10.3901/JME.2019.13.013

• 特邀专栏：航空发动机健康监测与故障诊断 • 上一篇下一篇

航空发动机快变信号的匹配同步压缩变换研究

陈雪峰¹, 王诗彬¹, 程礼²

1. 西安交通大学机械制造系统工程国家重点实验室西安 710049;
2. 空军工程大学航空航天工程学院西安 710038

收稿日期:2018-07-01 修回日期:2018-12-13 出版日期:2019-07-05 发布日期:2019-07-05
通讯作者: 陈雪峰(通信作者),男,1975年出生,博士,教授,博士研究生导师。主要研究方向为航空发动机、直升机、风电机组等重大装备运行安全保障的基础理论与应用。E-mail:chenxf@mail.xjtu.edu.cn
作者简介:王诗彬,男,1985年出生,博士,讲师,硕士研究生导师。主要研究方向为时频分析、稀疏信号处理等理论及其机械故障诊断应用。E-mail:wangshibin2008@mail.xjtu.edu.cn;程礼,男,1963出生,博士,教授,博士研究生导师。主要研究方向为航空发动机等重大装备运行安全保障的基础理论与应用。E-mail:cheng_qiaochu@foxmail.com
基金资助:
国家重点基础研究发展计划（973计划，2015CB057400）、国家自然科学基金（51605366）、中国博士后科学基金（2016M590937）和高校基本科研业务费资助项目。

Matching Synchrosqueezing Transform for Aero-engine's Signals with Fast Varying Instantaneous Frequency

CHEN Xuefeng¹, WANG Shibin¹, CHENG Li²

1. State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an 710049;
2. Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038

Received:2018-07-01 Revised:2018-12-13 Online:2019-07-05 Published:2019-07-05

摘要/Abstract

摘要： 航空发动机快变信号特征提取是航空发动机健康监测与故障诊断的关键技术之一。针对航空发动机快变信号特征提取需求及其瞬时频率快变的特点，提出了匹配同步压缩变换方法。构造了匹配快变信号调频结构的瞬时频率估计算子，能同时考虑快变信号时频能量随频率和时间方向的分布，从而提高快变信号时频表示的能量聚集性，提升航空发动机快变信号提取能力。通过航空发动机主轴承寿命试验机的碰摩故障试验，以及某型航空发动机的碰摩故障诊断工程案例，验证匹配同步压缩变换方法对于航空发动机快变信号处理的有效性。工程应用结果表明，对于航空发动机转子系统动静碰摩故障，匹配同步压缩变换能够有效提取该故障导致的振动信号瞬时频率快速振荡的强时变特征，从而诊断转子系统碰摩故障。

关键词: 故障诊断, 航空发动机, 时频分析, 同步压缩变换

Abstract: Feature extraction of aero-engine's vibration signals with fast time-varying instantaneous frequency (IF) plays an important role in health monitoring and fault diagnosis for aero-engines. According to the nonlinear frequency-modulated (FM) property of the aero-engine's vibration signals, a time-frequency analysis (TFA) method called matching synchrosqueezing transform (MSST) is introduced to achieve a highly concentrated TF representation comparable to the standard time-frequency reassignment methods (STFRM), even for signals with fast varying IF; furthermore, MSST retains the reconstruction benefit of synchrosqueezing transform (SST). MSST captures the philosophy of STFRM to simultaneously consider time and frequency variables, and incorporates three estimators (i.e., the IF estimator, the group delay estimator, and a chirp-rate estimator) into a comprehensive and accurate IF estimator. The effectiveness of MSST is verified by experimental and practical applications. The results of aero-engine engineering applications show that the feature of the fast varying IF caused by the rub-impact fault in an aero-engine rotor system can be effectively extracted and thus the rub-impact fault is diagnosed.

Key words: aero-engine, fault diagnosis, synchrosqueezing transform, time-frequency analysis

中图分类号:

TG17

陈雪峰, 王诗彬, 程礼. 航空发动机快变信号的匹配同步压缩变换研究[J]. 机械工程学报, 2019, 55(13): 13-22.

CHEN Xuefeng, WANG Shibin, CHENG Li. Matching Synchrosqueezing Transform for Aero-engine's Signals with Fast Varying Instantaneous Frequency[J]. Journal of Mechanical Engineering, 2019, 55(13): 13-22.

参考文献

[1] 陈光. 航空发动机结构设计分析[M]. 北京:北京航空航天大学出版社, 2014. CHEN Guang. Structural design and analysis for aero-engine[M]. Beijing:Beijing University of Aeronautics and Astronautics Press, 2014.
[2] CLARK C. F-35 Head bogdan explains the F135‘bad rub’ fix[OL].[2017-05-10]. http://breakingdefense.com/2014/06/jsf-fire-looks-like-isolated-event-f-35bs-should-flytomorrow/.
[3] WANG S, YANG L, CHEN X, et al. Nonlinear squeezing time-frequency transform and application in rotor rub-impact fault diagnosis[J]. Journal of Manufacturing Science and Engineering, 2017, 139(10):101005.
[4] 李应红, 尉询楷, 胡金海, 等. 航空发动机的智能诊断、建模与预测方法[M]. 北京:科学出版社, 2013. LI Yinghong, WEI Xunkai, HU Jinhai, et al. Intelligent diagnosis, modeling and prediction method for aero-engine[M]. Beijing:Science Press, 2013.
[5] VOLPONI A J. gas turbine engine health management:past, present, and future trends[J]. Journal of Engineering for Gas Turbines and Power, 2014, 136(5):051201.
[6] FENG Z, LIANG M, CHU F. Recent advances in time-frequency analysis methods for machinery fault diagnosis:A review with application examples[J]. Mech Syst Signal Process, 2013, 38(1):165-205.
[7] SEJDIC E, DJUROVIC I, JIANG J. Time-frequency feature representation using energy concentration:An overview of recent advances[J]. Digit Signal Process, 2009, 19(1):153-183.
[8] AUGER F, FLANDRIN P, LIN Y T, et al. Timefrequency reassignment and synchrosqueezing:An overview[J]. IEEE Signal Proccess Mag., 2013, 30(6):32-41.
[9] DAUBECHIES I, LU J, WU H T. Synchrosqueezed wavelet transforms:An empirical mode decompositionlike tool[J]. Appl. Comput. Harmon. A, 2011, 30(2):243-261.
[10] WANG S,CHEN X,CAI G,et al. Matching demodulation transform and synchrosqueezing in time-frequency analysis[J]. IEEE Trans Signal Process, 2014, 62(1):69-84.
[11] LI C, LIANG M. Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform[J]. Mech. Syst. Signal Process, 2012, 26:205-217.
[12] WANG S, CHEN X, TONG C, et al. Matching synchrosqueezing wavelet transform and application to aeroengine vibration monitoring[J]. IEEE Transactions on Instrumentation and Measurement, 2017, 66(2):360-372.
[13] YANG L,CHEN X,WANG S,et al. Rub-impact detection of rotor systems using time-frequency techniques[C/CD]//Proc. ASME 2016 International Mechanical Engineering Congress and Exposition, American Society of Mechanical Engineers, 2016.
[14] YANG L, CHEN X, WANG S. Mechanism of fast time-varying vibration for rotor-stator contact system:With application to fault diagnosis[J]. Journal of Vibration and Acoustics, 2017, 46:11-20.

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航空发动机快变信号的匹配同步压缩变换研究

Matching Synchrosqueezing Transform for Aero-engine's Signals with Fast Varying Instantaneous Frequency

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