Adaptive periodic mode decomposition and its application in rolling bearing fault diagnosis

Abstract As is known to all, rolling bearing fault will induce periodic impulses. Although the existing fault diagnosis methods, such as wavelet transform (WT) and ensemble empirical mode decomposition (EEMD), can effectively diagnose rolling bearing fault, their weak period recognition abilities limit their application in rolling bearing fault diagnosis. The Ramanujan subspace is introduced from the perspective of period recognition and extraction, and an adaptive periodic mode decomposition (APMD) method that can extract periodic components (PCs) without setting any parameters is proposed in this paper. APMD determines the major periods (MPs) in the signal by periodicity measurement, and projects the signal into respective Ramanujan subspaces of MPs to form the corresponding PCs. The analysis results of rolling bearing signals show that APMD has excellent ability to identify and extract PCs and is a valid method for rolling bearing fault diagnosis.

[1]  Shi-Wen Deng,et al.  Ramanujan subspace pursuit for signal periodic decomposition , 2015, ArXiv.

[2]  Rama Komaragiri,et al.  Heart rate monitoring and therapeutic devices: A wavelet transform based approach for the modeling and classification of congestive heart failure. , 2018, ISA transactions.

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

[4]  Shiv Dutt Joshi,et al.  Ramanujan Sums as Derivatives and Applications , 2018, IEEE Signal Processing Letters.

[5]  Jianhui Lin,et al.  Modified complementary ensemble empirical mode decomposition and intrinsic mode functions evaluation index for high-speed train gearbox fault diagnosis , 2018, Journal of Sound and Vibration.

[6]  P. P. Vaidyanathan.  Ramanujan-sum expansions for finite duration (FIR) sequences , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[7]  Devendra Kumar Yadav,et al.  Stacking Seismic Data Based on Ramanujan Sums , 2020, IEEE Geoscience and Remote Sensing Letters.

[8]  Keegan J. Moore,et al.  Wavelet-bounded empirical mode decomposition for measured time series analysis , 2018 .

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

[10]  William A. Sethares,et al.  Periodicity transforms , 1999, IEEE Trans. Signal Process..

[11]  Yang Li,et al.  Long short-term memory network with multi-resolution singular value decomposition for prediction of bearing performance degradation , 2020 .

[12]  P. P. Vaidyanathan,et al.  Ramanujan Sums in the Context of Signal Processing—Part I: Fundamentals , 2014, IEEE Transactions on Signal Processing.

[13]  Shi-Wen Deng,et al.  Signal Periodic Decomposition With Conjugate Subspaces , 2016, IEEE Transactions on Signal Processing.

[14]  Cai Yi,et al.  Sparsity guided empirical wavelet transform for fault diagnosis of rolling element bearings , 2018 .

[15]  Huibin Lin,et al.  Sliding window denoising K-Singular Value Decomposition and its application on rolling bearing impact fault diagnosis , 2018 .

[16]  P. P. Vaidyanathan.  Ramanujan Sums in the Context of Signal Processing—Part II: FIR Representations and Applications , 2014, IEEE Transactions on Signal Processing.

[17]  Jian Cheng,et al.  A noise reduction method based on adaptive weighted symplectic geometry decomposition and its application in early gear fault diagnosis , 2021 .

[18]  Tao Yu,et al.  An improved empirical mode decomposition method using second generation wavelets interpolation , 2018, Digit. Signal Process..

[19]  Jianhui Lin,et al.  A novel method based on Adaptive Periodic Segment Matrix and Singular Value Decomposition for removing EMG artifact in ECG signal , 2020, Biomed. Signal Process. Control..

[20]  Lei Xie,et al.  Median ensemble empirical mode decomposition , 2020, Signal Process..

[21]  Karthikeyan Umapathy,et al.  Time-Frequency Analysis via Ramanujan Sums , 2012, IEEE Signal Processing Letters.

[22]  Rama Komaragiri,et al.  Design of wavelet transform based electrocardiogram monitoring system. , 2018, ISA transactions.

[23]  Lixiao Cao,et al.  Multi-source feature extraction of rolling bearing compression measurement signal based on independent component analysis , 2021 .

[24]  Paolo Pennacchi,et al.  Rolling element bearing diagnosis based on singular value decomposition and composite squared envelope spectrum , 2021 .

[25]  Thomas W. Parks,et al.  Orthogonal, exactly periodic subspace decomposition , 2003, IEEE Trans. Signal Process..

[26]  Shesheng Gao,et al.  Maximum likelihood principle and moving horizon estimation based adaptive unscented Kalman filter , 2018 .

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