Gearbox compound fault diagnosis based on a combined MSGMD–MOMEDA method

Weak fault detection is a complex and challenging task when two or more faults (i.e. a compound fault) with discordant severity occur in different parts of a gearbox. The weak fault features are prone to be submerged by the severe fault features and strong background noise, which easily lead to a missed diagnosis. To solve this problem, a novel diagnosis method combining multi-symplectic geometry mode decomposition (MSGMD) and multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) is proposed for gearbox compound fault in this paper. Specifically, different fault components are separated by an improved symplectic geometry mode decomposition (SGMD), namely the MSGMD method. The weak fault features are enhanced by the MOMEDA. In the process of research, a new scheme of selecting the key parameters for MOMEDA is proposed, which is a key step in the practical application of MOMEDA. Compared with SGMD, the proposed MSGMD has two main improvements, including suppressing mode mixing and preventing the generation of pseudo-components. Compared with the original method of selecting parameters based on multipoint kurtosis, the proposed MOMEDA parameter-selecting scheme has the merits of higher accuracy and greater precision. The analysis results of two cases, of simulation and an experiment signal, reveal that the MSGMD–MOMEDA method can accurately diagnose gearbox compound fault even under strong background noise.

[1]  Zhiwen Liu,et al.  An improved local characteristic-scale decomposition to restrict end effects, mode mixing and its application to extract incipient bearing fault signal , 2021, Mechanical Systems and Signal Processing.

[2]  M. Liang,et al.  Analytical vibration signal model and signature analysis in resonance region for planetary gearbox fault diagnosis , 2021 .

[3]  Jianqun Zhang,et al.  An FSK-MBCNN based method for compound fault diagnosis in wind turbine gearboxes , 2021 .

[4]  Chang Liu,et al.  Research on bearing fault diagnosis based on spectrum characteristics under strong noise interference , 2021 .

[5]  Yibing Liu,et al.  Fault detection of planetary subassemblies in a wind turbine gearbox using TQWT based sparse representation , 2021 .

[6]  Ling Zhao,et al.  The Extraction Method of Gearbox Compound Fault Features Based on EEMD and Cloud Model , 2020, Mathematical Problems in Engineering.

[7]  Dong Quan,et al.  A weak fault diagnosis scheme for common rail injector based on MGOA-MOMEDA and improved hierarchical dispersion entropy , 2020, Measurement Science and Technology.

[8]  Qiang Miao,et al.  Cyclic Harmonic Ratio Defined in Squared Envelope Spectrum and Log-Envelope Spectrum for Gearbox Fault Diagnosis , 2020, IEEE Transactions on Instrumentation and Measurement.

[9]  Haiyang Pan,et al.  Maximum envelope-based Autogram and symplectic geometry mode decomposition based gear fault diagnosis method , 2020 .

[10]  Zhiyuan He,et al.  An optimal filter length selection method for MED based on autocorrelation energy and genetic algorithms. , 2020, ISA transactions.

[11]  Hongkun Li,et al.  Identification of planetary gearbox weak compound fault based on parallel dual-parameter optimized resonance sparse decomposition and improved MOMEDA , 2020 .

[12]  Ming Zhao,et al.  Application of an improved MCKDA for fault detection of wind turbine gear based on encoder signal , 2020 .

[13]  Yu Xin,et al.  A novel compound fault diagnosis method using intrinsic component filtering , 2020, Measurement Science and Technology.

[14]  Jingyue Wang,et al.  Composite fault diagnosis of gearbox based on empirical mode decomposition and improved variational mode decomposition , 2020 .

[15]  Yao Cheng,et al.  Adaptive Multipoint Optimal Minimum Entropy Deconvolution Adjusted and Application to Fault Diagnosis of Rolling Element Bearings , 2019, IEEE Sensors Journal.

[16]  Konstantinos Gryllias,et al.  Vibration Based Condition Monitoring of Helicopter Gearboxes Based on Cyclostationary Analysis , 2019, Volume 6: Ceramics; Controls, Diagnostics, and Instrumentation; Education; Manufacturing Materials and Metallurgy.

[17]  Jianhui Lin,et al.  Modal Parameters Identification Method Based on Symplectic Geometry Model Decomposition , 2019, Shock and Vibration.

[18]  Yibing Liu,et al.  Compound faults diagnosis and analysis for a wind turbine gearbox via a novel vibration model and empirical wavelet transform , 2019, Renewable Energy.

[19]  Jinde Zheng,et al.  Detection for Incipient Damages of Wind Turbine Rolling Bearing Based on VMD-AMCKD Method , 2019, IEEE Access.

[20]  Jun Ma,et al.  A Parameter Adaptive MOMEDA Method Based on Grasshopper Optimization Algorithm to Extract Fault Features , 2019, Mathematical Problems in Engineering.

[21]  Haiyang Pan,et al.  Symplectic geometry mode decomposition and its application to rotating machinery compound fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[22]  Dejie Yu,et al.  Compound fault diagnosis of gearboxes based on GFT component extraction , 2016 .

[23]  Min Wang,et al.  A method for the compound fault diagnosis of gearboxes based on morphological component analysis , 2016 .

[24]  Feng Jia,et al.  Early Fault Diagnosis of Bearings Using an Improved Spectral Kurtosis by Maximum Correlated Kurtosis Deconvolution , 2015, Sensors.

[25]  Dejie Yu,et al.  Energy operator demodulating of optimal resonance components for the compound faults diagnosis of gearboxes , 2015 .

[26]  Alan Wee-Chung Liew,et al.  Symplectic geometry spectrum analysis of nonlinear time series , 2014, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[27]  Joël M. H. Karel,et al.  Singular Spectrum Decomposition: a New Method for Time Series Decomposition , 2014, Adv. Data Sci. Adapt. Anal..

[28]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[29]  Qing Zhao,et al.  Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection , 2012 .

[30]  Jing Lin,et al.  Identification of mechanical compound-fault based on the improved parameter-adaptive variational mode decomposition. , 2019, ISA transactions.

[31]  Qing Zhao,et al.  Multipoint Optimal Minimum Entropy Deconvolution and Convolution Fix: Application to vibration fault detection , 2017 .

[32]  Jérôme Antoni,et al.  Vibration based condition monitoring of a multistage epicyclic gearbox in lifting cranes , 2014 .