Nonlinear sparse mode decomposition and its application in planetary gearbox fault diagnosis

Abstract Traditional time-frequency analysis methods, including empirical mode decomposition (EMD), local characteristic-scale decomposition (LCD) and variable mode decomposition (VMD), have some limitations in nonlinear signal analysis. When the signal has strong noise, traditional time-frequency analysis methods will force the signal to be decomposed into several inaccurate components, and the achieved components usually suffer from the end effect problem. Considering the above pressing challenge, a new signal decomposition algorithm, nonlinear sparse mode decomposition (NSMD), is proposed in this protocol. The core of NSMD is that the local narrowband signal disappears under the action of the singular local linear operator, so the singular local linear operator can be applied to extract the local narrowband component of the detected signal. Meanwhile, the obtained local narrowband signal can be superposed as the basic signal to close to the original signal, realizing the adaptive decomposition of the signal with good robustness and adaptability. The analysis results of simulation signals and planetary gearbox fault signals indicate that the proposed NSMD method is effective for raw vibration signals.

[1]  Yonghao Miao,et al.  Application of sparsity-oriented VMD for gearbox fault diagnosis based on built-in encoder information. , 2020, ISA transactions.

[2]  Jorge Chuya Sumba,et al.  Bearing Fault Diagnosis Based on Optimal Time-Frequency Representation Method , 2019, IFAC-PapersOnLine.

[3]  Jinfeng Zhang,et al.  Periodic impulses extraction based on improved adaptive VMD and sparse code shrinkage denoising and its application in rotating machinery fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[4]  Zhipeng Feng,et al.  Induction motor stator current analysis for planetary gearbox fault diagnosis under time-varying speed conditions , 2020 .

[5]  Junsheng Cheng.  Local Characteristic-scale Decomposition Method and Its Application to Gear Fault Diagnosis , 2012 .

[6]  Zhongmin Deng,et al.  An improved EMD method based on the multi-objective optimization and its application to fault feature extraction of rolling bearing , 2017 .

[7]  Yang Yang,et al.  Intrinsic chirp component decomposition by using Fourier Series representation , 2017, Signal Process..

[8]  Li Ma,et al.  A roller bearing fault diagnosis method based on the improved ITD and RRVPMCD , 2014 .

[9]  Sridhar Krishnan,et al.  Quantification of fragmented QRS complex using intrinsic time-scale decomposition , 2017, Biomed. Signal Process. Control..

[10]  Wenxian Yang,et al.  A Novel Fault Detection Scheme Using Improved Inherent Multiscale Fuzzy Entropy With Partly Ensemble Local Characteristic-Scale Decomposition , 2020, IEEE Access.

[11]  Changqing Shen,et al.  Initial center frequency-guided VMD for fault diagnosis of rotating machines , 2018, Journal of Sound and Vibration.

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

[13]  Xiaodong Wang,et al.  An improved local mean decomposition method based on improved composite interpolation envelope and its application in bearing fault feature extraction. , 2019, ISA transactions.

[14]  Tao Liu,et al.  Extreme-point weighted mode decomposition , 2018, Signal Process..

[15]  Qingwei Gao,et al.  A detection method for bearing faults using null space pursuit and S transform , 2014, Signal Process..

[16]  Mohamed Benbouzid,et al.  An imbalance fault detection method based on data normalization and EMD for marine current turbines. , 2017, ISA transactions.

[17]  Jun Wang,et al.  An intelligent diagnosis scheme based on generative adversarial learning deep neural networks and its application to planetary gearbox fault pattern recognition , 2018, Neurocomputing.

[18]  Kun Yu,et al.  Second order multi-synchrosqueezing transform for rub-impact detection of rotor systems , 2019, Mechanism and Machine Theory.

[19]  Wang Jindong,et al.  A compound interpolation envelope local mean decomposition and its application for fault diagnosis of reciprocating compressors , 2018, Mechanical Systems and Signal Processing.

[20]  Qi Jin,et al.  Planetary gearbox fault feature learning using conditional variational neural networks under noise environment , 2019, Knowl. Based Syst..

[21]  Jie Chen,et al.  Performance degradation assessment of a wind turbine gearbox based on multi-sensor data fusion , 2019, Mechanism and Machine Theory.

[22]  Qiang Miao,et al.  Time–frequency analysis based on ensemble local mean decomposition and fast kurtogram for rotating machinery fault diagnosis , 2018 .

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

[24]  Shiqian Chen,et al.  Fault diagnosis of planetary gearbox under variable-speed conditions using an improved adaptive chirp mode decomposition , 2020 .

[25]  I. Osorio,et al.  Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[26]  Anand Parey,et al.  Extraction of weak fault transients using variational mode decomposition for fault diagnosis of gearbox under varying speed , 2020 .

[27]  Xin Li,et al.  An early fault diagnosis method of gear based on improved symplectic geometry mode decomposition , 2020 .