Interpretable Intelligent Diagnosis Method for Aero-engines Based on Deep Signal Separation

doi:10.3901/JME.2024.12.077

Abstract

Abstract: To address the problem that existing harmonic extraction methods such as time-frequency analysis and signal decomposition cannot adaptively extract the rotational speed harmonic components with clear physical meaning when performing tacho-less order tracking(TLOT) applications for rotating machineries, an interpretable rotational speed harmonic signal adaptive separation method based on a time-frequency spatial deep binary mask model is proposed. Firstly, a series of simulated template signals with clear physical meaning of the instantaneous frequency(IF) change of the tacho harmonics are constructed as training samples for the deep binary mask model, and then the time-frequency representation(TFR) of the tacho fundamental harmonics and higher-order harmonics of the constructed simulated template signals are used to generate the training target of the binary mask model in the time-frequency(TF) space of the fundamental harmonics. The non-linear mapping relationship between the TF space of the simulated template signal and the TF image of the tacho fundamental harmonics is established to achieve the accurate separation and extraction of the tacho fundamental harmonic components and the credible masking suppression of the tacho higher-order harmonic components, ensuring that the harmonic components obtained by the deep binary mask model have a clear physical meaning. Then, the instantaneous phase information related to the deep nonlinear mapping of the rotational speed signal is obtained by the Hilbert transform of the fundamental harmonics obtained by deep separation, and applied to the original signal with equal angle resampling to accomplish accurate order tracking. In summary, the proposed method overcomes the problem that the traditional TLOT method relies heavily on expert experience, which can provide instantaneous phase support with clear physical meaning for TLOT of the transmission system.

Key words: deep signal separation model, time-frequency spatial binary mask, instantaneous phase information, variable speed condition, tacho-less order tracking, interpretable deep model

CLC Number:

WANG Yi, DING Jiakai, SUN Haoran, QIN Yi, TANG Baoping. Interpretable Intelligent Diagnosis Method for Aero-engines Based on Deep Signal Separation[J]. Journal of Mechanical Engineering, 2024, 60(12): 77-89.

References

[1] 曹明，王鹏，左洪福，等. 民用航空发动机故障诊断与健康管理现状、挑战与机遇Ⅱ：地面综合诊断、寿命管理和智能维护维修决策[J]. 航空学报，2022，43(9)：34-73. CAO Ming，WANG Peng，ZUO Hongfu，et al. Current status，challenges and opportunities of civil aero-engine diagnostics and health management Ⅱ：Comprehensive off-board diagnosis，life management and intelligent condition based MRO[J]. Acta Aeronautica et Astronautica Sinica，2022，43(9)：34-73.
[2] 张忠强，张新，王家序，等. 基于重加权谱峭度方法的航空发动机故障诊断[J]. 航空学报，2022，43(9)：140-149. ZHANG Zhongqiang，ZHANG Xin，WANG Jiaxu，et al. Re-weighted kurtogram for aero-engine fault diagnosis[J]. Acta Aeronautica et Astronautica Sinica，2022，43(9)：140-149.
[3] 《航空发动机设计手册》总编委会. 航空发动机设计手册：第12册[M]. 北京：航空工业出版社，2002. Editorial Board for Aeroengine Design Manual. Aeroengine design manual：Vol. 12[M]. Beijing：Aviation Industry Press，2002.
[4] 曹明，黄金泉，周健，等. 民用航空发动机故障诊断与健康管理现状、挑战与机遇Ⅰ：气路、机械和FADEC系统故障诊断与预测[J]. 航空学报，2022，43(9)：21-33. CAO Ming，HUANG Jinquan，ZHOU Jian，et al. Current status，challenges and opportunities of civil aero-engine diagnostics and health management Ⅰ：Diagnosis and prognosis of engine gas path，mechanical and FADEC [J]. Acta Aeronautica et Astronautica Sinica，2022，43(9)：21-33.
[5] 杨语. 航空发动机附件机匣传动齿轮失效分析研究[D].重庆：重庆大学，2016. YANG Yu. Failure analysis of aero-engine accessory gearbox[D]. Chongqing：Chongqing University，2016.
[6] 史妍妍. 基于热分析的附件机匣若干可靠性问题研究[D]. 沈阳：东北大学，2009. SHI Yanyan. Research on some reliability problems of accessory casing based on thermal analysis[D]. Shenyang：Northeastern University，2009.
[7] ANTONI J. Cyclic spectral analysis of rolling-element bearing signals: Facts and fictions[J]. Journal of Sound and Vibration，2007，304(3-5)：497-529.
[8] LEI Y，LIN J，ZUO M J，et al. Condition monitoring and fault diagnosis of planetary gearboxes：A review[J]. Measurement，2014，48(2)：292-305.
[9] 郭瑜，秦树人. 无转速计旋转机械升降速振动信号零相位阶比跟踪滤波[J]. 机械工程学报，2004，40(3)：50-54. GUO Yu，QIN Shuren. Tacholess order tracking filtering for run-up or coast down vibration signal of rotating machinery based on zero-phase distortion digital filtering[J]. Chinese Journal of Mechanical Engineering，2004，40(3)：50-54.
[10] 汤宝平，何启源，魏玉果，等. 基于角加速度的复合计算阶次跟踪方法[J]. 机械工程学报，2008，44(8)：143-147. TANG Baoping，HE Qiyuan，WEI Yuguo，et al. Hybrid computed order tracking method based on angular acceleration[J]. Chinese Journal of Mechanical Engineering，2008，44(8)：143-147.
[11] RÉMOND D，ANTONI J，RANDALL R. Editorial for the special issue on instantaneous angular speed (IAS) processing and angular applications[J]. Mechanical Sys-tems and Signal Processing，2014，44(1-2)：1-4.
[12] WANG Y，TANG B P，QIN Y，et al. Rolling bearing fault detection of civil aircraft engine based on adaptive estimation of instantaneous angular speed[J]. IEEE Transactions on Industrial Informatics，2019，16(7)：4938-4948.
[13] BLOUGH J R. Development and analysis of time variant discrete Fourier transform order tracking[J]. Mechanical Systems and Signal Processing，2003，17(6)：1185-1199.
[14] BONNARDOT F，EL BADAOUI M，RANDALL R B，et al. Use of the acceleration signal of a gearbox in order to perform angular resampling (with limited speed fluctuation)[J]. Mechanical Systems and Signal Processing，2005，19(4)：766-785.
[15] WANG Y，PETER W T，TANG B P，et al. Order spectrogram visualization for rolling bearing fault detection under speed variation conditions[J]. Mechanical Systems and Signal Processing，2019，122(5)：580-596.
[16] PEETERS C，LECLERE Q，ANTONI J，et al. Review and comparison of tacholess instantaneous speed estimation methods on experimental vibration data[J]. Mechanical Systems and Signal Processing，2019，129(3)：407-436.
[17] ZHAO M，LIN J，XU X Q，et al. Tacholess envelope order analysis and its application to fault detection of rolling element bearings with varying speeds[J]. Sensors，2013，13(8)：10856-10875.
[18] COMBET F，GELMAN L. An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor[J]. Mechanical Systems and Signal Processing，2007，21(6)：2590-2606.
[19] RANDALL R B，SAWALHI N，COATS M. Separation of gear and bearing fault signals from a wind turbine transmission under varying speed and load[M]. Berlin：Springer，2012.
[20] FENG Z P，CHU F L，ZUO M J. Time-frequency analysis of time-varying modulated signals based on improved energy separation by iterative generalized demodulation[J]. Journal of Sound and Vibration，2011，330(6)：1225-1243.
[21] ZHAO M，LIN J，WANG X F，et al. A tacho-less order tracking technique for large speed variations[J]. Mechanical Systems and Signal Processing，2013，40(1)：76-90.
[22] LI X，CHENG J，SHAO H D，et al. A fusion CWSMM-based framework for rotating machinery fault diagnosis under strong interference and imbalanced case[J]. IEEE Transactions on Industrial Informatics，2021，18(8)：5180-5189.
[23] CHENG Y W，HU K，WU J，et al. Autoencoder quasi-recurrent neural networks for remaining useful life prediction of engineering systems[J]. IEEE/ASME Transactions on Mechatronics，2021，27(2)：1081-1092.
[24] ILIADIS M，SPINOULAS L，KATSAGGELOS A K. Deepbinarymask：Learning a binary mask for video compressive sensing[J]. Digital Signal Processing，2020，96：102591.
[25] MICHELSANTI D，TAN Z H，SIGURDSSON S，et al. Deep-learning-based audio-visual speech enhancement in presence of Lombard effect[J]. Speech Communication，2019，115(8)：38-50.
[26] ANTONI J，GRIFFATON J，ANDRÉ H，et al. Feedback on the Surveillance 8 challenge: Vibration-based diagnosis of a Safran aircraft engine[J]. Mechanical Systems and Signal Processing，2017，97(10)：112-144.
[27] 张光耀，王义，李晓蒙，等. 基于自适应谐波分量提取的航空发动机附件传动系统变速故障诊断方法[J]. 仪器仪表学报，2023，44(5)：10-20. ZHANG Guangyao，WANG Yi，LI Xiaomeng，et al. A speed-varying fault diagnosis method for the aero-engine accessory transmission system based on adaptive harmonic components extraction[J]. Chinese Journal of Scientific Instrument，2023，44(5)：10-20.