A novel sparse feature extraction method based on sparse signal via dual-channel self-adaptive TQWT

Abstract Sparse signal is a kind of sparse matrices which can carry fault information and simplify the signal at the same time. This can effectively reduce the cost of signal storage, improve the efficiency of data transmission, and ultimately save the cost of equipment fault diagnosis in the aviation field. At present, the existing sparse decomposition methods generally extract sparse fault characteristics signals based on orthogonal basis atoms, which limits the adaptability of sparse decomposition. In this paper, a self-adaptive atom is extracted by the improved dual-channel tunable Q-factor wavelet transform (TQWT) method to construct a self-adaptive complete dictionary. Finally, the sparse signal is obtained by the orthogonal matching pursuit (OMP) algorithm. The atoms obtained by this method are more flexible, and are no longer constrained to an orthogonal basis to reflect the oscillation characteristics of signals. Therefore, the sparse signal can better extract the fault characteristics. The simulation and experimental results show that the self-adaptive dictionary with the atom extracted from the dual-channel TQWT has a stronger decomposition freedom and signal matching ability than orthogonal basis dictionaries, such as discrete cosine transform (DCT), discrete Hartley transform (DHT) and discrete wavelet transform (DWT). In addition, the sparse signal extracted by the self-adaptive complete dictionary can reflect the time-domain characteristics of the vibration signals, and can more accurately extract the bearing fault feature frequency.

[1]  Bo Jing,et al.  Impulse feature extraction method for machinery fault detection using fusion sparse coding and online dictionary learning , 2015 .

[2]  Lingli Cui,et al.  A three-dimensional geometric features-based SCA algorithm for compound faults diagnosis , 2019, Measurement.

[3]  Douzhe Li,et al.  DME Interference mitigation for L-DACS1 based on system identification and sparse representation , 2016 .

[4]  Konstantinos Gryllias,et al.  Mechanical fault diagnosis using Convolutional Neural Networks and Extreme Learning Machine , 2019, Mechanical Systems and Signal Processing.

[5]  Lingli Cui,et al.  A novel feature extraction method for roller bearing using sparse decomposition based on self-Adaptive complete dictionary , 2019 .

[6]  Chunlin Zhang,et al.  Multi-faults diagnosis of rolling bearings via adaptive customization of flexible analytical wavelet bases , 2020 .

[7]  Huaqing Wang,et al.  Underdetermined Source Separation of Bearing Faults Based on Optimized Intrinsic Characteristic-Scale Decomposition and Local Non-Negative Matrix Factorization , 2019, IEEE Access.

[8]  Zuozhou Pan,et al.  A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings , 2020 .

[9]  Deanna Needell,et al.  Uniform Uncertainty Principle and Signal Recovery via Regularized Orthogonal Matching Pursuit , 2007, Found. Comput. Math..

[10]  Jipu Li,et al.  A Robust Weight-Shared Capsule Network for Intelligent Machinery Fault Diagnosis , 2020, IEEE Transactions on Industrial Informatics.

[11]  Pabitra Mitra,et al.  Removal of Eye Blink Artifacts From EEG Signals Using Sparsity , 2018, IEEE Journal of Biomedical and Health Informatics.

[12]  Lingli Cui,et al.  Fault Severity Classification and Size Estimation for Ball Bearings Based on Vibration Mechanism , 2019, IEEE Access.

[13]  Lingli Cui,et al.  A Novel Weighted Sparse Representation Classification Strategy Based on Dictionary Learning for Rotating Machinery , 2020, IEEE Transactions on Instrumentation and Measurement.

[14]  Fulin Wang,et al.  Vibration response and fault characteristics analysis of gear based on time-varying mesh stiffness , 2020 .

[15]  Siliang Lu,et al.  Sound-aided vibration weak signal enhancement for bearing fault detection by using adaptive stochastic resonance , 2019, Journal of Sound and Vibration.

[16]  Jianfeng Ma,et al.  Research on Remaining Useful Life Prediction of Rolling Element Bearings Based on Time-Varying Kalman Filter , 2020, IEEE Transactions on Instrumentation and Measurement.

[17]  Huaqing Wang,et al.  Step-by-Step Compound Faults Diagnosis Method for Equipment Based on Majorization-Minimization and Constraint SCA , 2019, IEEE/ASME Transactions on Mechatronics.

[18]  Konstantinos Gryllias,et al.  Intelligent Fault Diagnosis for Rotary Machinery Using Transferable Convolutional Neural Network , 2020, IEEE Transactions on Industrial Informatics.

[19]  Lingli Cui,et al.  An Enhanced Intelligent Diagnosis Method Based on Multi-Sensor Image Fusion via Improved Deep Learning Network , 2020, IEEE Transactions on Instrumentation and Measurement.

[20]  Laura Rebollo-Neira,et al.  Wavelet based dictionaries for dimensionality reduction of ECG signals , 2018, Biomed. Signal Process. Control..

[21]  Haifeng Liu,et al.  Sparse signal recovery via alternating projection method , 2018, Signal Process..

[22]  Geert Leus,et al.  Deterministic fourier-based dictionary design for sparse reconstruction , 2016, 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM).

[23]  Xuefeng Chen,et al.  Sparse representation based on parametric impulsive dictionary design for bearing fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[24]  Fangyi Wan,et al.  Rotating machinery fault diagnosis based on convolutional neural network and infrared thermal imaging , 2020, Chinese Journal of Aeronautics.

[25]  Hongli Gao,et al.  A new bearing fault diagnosis method based on modified convolutional neural networks , 2020, Chinese Journal of Aeronautics.

[26]  David He,et al.  Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning , 2020, Chinese Journal of Aeronautics.

[27]  Peng Chen,et al.  Automatic Patrol and Inspection Method for Machinery Diagnosis Robot—Sound Signal-Based Fuzzy Search Approach , 2020, IEEE Sensors Journal.

[28]  Sandeep Raj,et al.  Sparse representation of ECG signals for automated recognition of cardiac arrhythmias , 2018, Expert Syst. Appl..

[29]  Guozheng Li,et al.  Blind source separation of composite bearing vibration signals with low-rank and sparse decomposition , 2019, Measurement.

[30]  Ping Wang,et al.  Orthogonal sparse dictionary based on Chirp echo for ultrasound imaging , 2019 .