Cooperative Detection and Decomposition Approach of Deviation-included Frequency Components for Mechanical Transmission Systems

doi:10.3901/JME.2024.15.100

Abstract

Abstract: Fault diagnosis of transmission systems plays an essential role in ensuring the reliable operation of equipment, and signal decomposition is an important technique in fault feature extraction. Due to the existence of transmission error, component inertia, and frequency resolution, the tested frequency of the vibration signal component often deviates from the theoretical one calculated according to the kinematic relationship, and the existence determination of the components is usually ignored. The above limitations result in common challenges for the mainstream signal decomposition approaches in terms of decomposition accuracy and physical interpretability for decomposing deviation-included frequency components. Therefore, a deviation-included frequency component cooperative detection and decomposition approach is proposed to detect the deviation-included frequency components and determine their existence. The cooperative detection results can be further used as the basis of signal decomposition, and the decomposition accuracy is greatly improved compared with the existing methods without cooperative detection. Finally, three practical application cases demonstrate the effectiveness of the proposed method in transmission system signal characteristics extraction under stationary and non-stationary conditions.

Key words: cooperative detection, fault diagnosis, transmission systems, signal decomposition

CLC Number:

TG156

YU Xiaoluo, YANG Yang, DU Minggang, HE Qingbo, PENG Zhike. Cooperative Detection and Decomposition Approach of Deviation-included Frequency Components for Mechanical Transmission Systems[J]. Journal of Mechanical Engineering, 2024, 60(15): 100-112.

References

[1] YU Xiaoluo，LI Zhanwei，HE Qingbo，et al.Gearbox fault diagnosis based on bearing dynamic force identification[J].Journal of Sound and Vibration，2021，511:116360.
[2] LECLÉRE Q，ANDRÉ H，ANTONI J.A multi-order probabilistic approach for instantaneous angular speed tracking debriefing of the CMMNO'14 diagnosis contest[J].Mechanical Systems and Signal Processing，2016，81:375-386.
[3] FAKHER C，WALTER B，RADOSLAW Z，et al.Gearbox vibration signal amplitude and frequency modulation[J].Shock and Vibration，2015，19(4):635-652.
[4] HE Guolin，LI Jianlin，DING Kang，et al.Feature extraction of gear and bearing compound faults based on vibration signal sparse decomposition[J].Applied Acoustics，2022，189:108604.
[5] CHEN Shiqian，DONG Xingjian， PENG Zhike， et al.Nonlinear chirp mode decomposition:A variational method[J].IEEE Transactions on Signal Processing，2017，65(22):6024-6037.
[6] FENG Zhipeng，ZHOU Yakai，ZUO Mingjian，et al.Atomic decomposition and sparse representation for complex signal analysis in machinery fault diagnosis:A review with examples[J].Measurement，2017，103:106-132.
[7] ELBOUCHIKHI E，CHOQUEUSE V，AMIRAT Y，et al.An efficient hilbert–huang transform-based bearing faults detection in induction machines[J].IEEE Transactions on Energy Conversion，2017，32(2):401-413.
[8] PAN Haiyang，ZHENG Jinde，YANG Yu，et al.Nonlinear sparse mode decomposition and its application in planetary gearbox fault diagnosis[J].Mechanism and Machine Theory，2021，155:104082.
[9] CHEN Shiqian，PENG Zhike，YANG Yang，et al.Intrinsic chirp component decomposition by using Fourier series representation[J].Signal Processing，2017，137:319-327.
[10] CHEN Shiqian，YANG Yang，PENG Zhike，et al.Detection of rub-impact fault for rotor-stator systems:A novel method based on adaptive chirp mode decomposition[J].Journal of Sound and Vibration，2019，440:83-99.
[11] CHEN Shiqian，WANG Kaiyun，CHANG Chao，et al.A two-level adaptive chirp mode decomposition method for the railway wheel flat detection under variable-speed conditions[J].Journal of Sound and Vibration，2021，498:115963.
[12] CHEN Shiqian，DU Minggang，PENG Zhike，et al.Fault diagnosis of planetary gearbox under variable-speed conditions using an improved adaptive chirp mode decomposition[J].Journal of Sound and Vibration，2020，468:115065.
[13] ZHENG Jinde，SU Miaoxian，YING Wanming，et al.Improved uniform phase empirical mode decomposition and its application in machinery fault diagnosis[J].Measurement，2021，179:109425.
[14] WANG Zhijian，YANG Ningning，LI Naipeng，et al.A new fault diagnosis method based on adaptive spectrum mode extraction[J].Structural Health Monitoring，2021，20(6):3354-3370.
[15] CHENG Jian，YANG Yu，LI Xin，et al.Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis[J].Mechanical Systems and Signal Processing，2021，161:107943.
[16] ZHAO Zhibin，WANG Shibin，WONG D，et al.Fast sparsity-assisted signal decomposition with non-convex enhancement for bearing fault diagnosis[J].IEEE/ASME Transactions on Mechatronics，2022，27(4):2333-2344.
[17] 李硕，李天昀.短波信道下的跳频信号检测[J].电子学报，2019，47(3):623-629.LI Shuo，LI Tianyun.Frequency hopping signal detection over the short-wave channel[J].Acta Electronica Sinica，2019，47(3):623-629.
[18] JAFARNIA J，BROUMANDAN A，NIELSEN J，et al.GPS spoofer countermeasure effectiveness based on signal strength, noise power, and C/N0 measurements[J].International Journal of Satellite Communications and Networking，2012，30(4):181-191.
[19] 段德鑫，姜莹，刘宗伟，等.基于图像处理的回声定位信号检测方法[J].海洋科学进展，2022，40(1):145-153.DUAN Dexin，JIANG Ying，LIU Zongwei，et al.Detection method for echolocation clicks based on image processing[J].Advances in Marine Science，2022，40(1):145-153.
[20] VILLA L，REÑONES A，PERÁN J，et al.Angular resampling for vibration analysis in wind turbines under non-linear speed fluctuation[J].Mechanical Systems and Signal Processing，2011，25(6):2157-2168.
[21] FYFE K，MUNCK E.Analysis of computed order tracking[J].Mechanical Systems and Signal Processing，1997，11(2):187-205.
[22] JI Mengyu，PENG Gaoliang，HE Jun，et al.A two-stage，intelligent bearing-fault-diagnosis method using order-tracking and a one-dimensional convolutional neural network with variable speeds[J].Sensors，2021，21(3):675.
[23] ZHAO Sifang，SONG Qiang，WANG Mingsheng，et al.Mechanical faults detection for vehicle motors under nonstationary conditions based on vold-kalman order tracking method[J].Proceedings of the Institution of Mechanical Engineers，Part D:Journal of Automobile Engineering，2022，237(4):839-851.
[24] BOYD S，PARIKH N，CHU E，et al.Distributed optimization and statistical learning via the alternating direction method of multipliers[J].Foundations and Trends in Machine Learning，2010，3(1):1-122.
[25] Patuxent River MD.Helicopter drivetrain vibration condition monitoring algorithms[EB/OL].[2022-03-15].http://qsun.eng.ua.edu/cpw_web_new/geardes.htm.