Rolling Bearing Feature Extraction Method Based on Improved Intrinsic Time-Scale Decomposition and Mathematical Morphological Analysis

To overcome the difficulty of extracting the feature frequency of early bearing faults, this paper proposes an adaptive feature extraction scheme. First, the improved intrinsic time-scale decomposition, proposed in this paper, is used as a noise reduction method. Then, we use the adaptive composite quantum morphology analysis method, also proposed in this paper, to perform an adaptive demodulation analysis on the signal, and finally, extract the fault characteristics in the envelope spectrum. The experimental results show that the scheme performs well in the early fault feature extraction of rolling bearings.

[1]  Mohamed Benbouzid,et al.  EEMD-based notch filter for induction machine bearing faults detection , 2018 .

[2]  Xiaodong Jia,et al.  A novel strategy for signal denoising using reweighted SVD and its applications to weak fault feature enhancement of rotating machinery , 2017 .

[3]  QINGBIN TONG,et al.  A Fault Diagnosis Approach for Rolling Element Bearings Based on RSGWPT-LCD Bilayer Screening and Extreme Learning Machine , 2017, IEEE Access.

[4]  Jianpeng Ma,et al.  An improved intrinsic time-scale decomposition method based on adaptive noise and its application in bearing fault feature extraction , 2020, Measurement Science and Technology.

[5]  Yimin Shao,et al.  Fault feature extraction of rotating machinery using a reweighted complete ensemble empirical mode decomposition with adaptive noise and demodulation analysis , 2020 .

[6]  Chao Cai,et al.  Despeckling of medical ultrasound images based on quantum-inspired adaptive threshold , 2010 .

[7]  Yaguo Lei,et al.  Applications of stochastic resonance to machinery fault detection: A review and tutorial , 2019, Mechanical Systems and Signal Processing.

[8]  Yongbo Li,et al.  Application of Bandwidth EMD and Adaptive Multiscale Morphology Analysis for Incipient Fault Diagnosis of Rolling Bearings , 2017, IEEE Transactions on Industrial Electronics.

[9]  Fengrong Bi,et al.  Knock detection in spark ignition engines based on complementary ensemble improved intrinsic time-scale decomposition (CEIITD) and Bi-spectrum , 2018 .

[10]  Jingxiang Lv,et al.  Weak Fault Feature Extraction of Rolling Bearings Using Local Mean Decomposition-Based Multilayer Hybrid Denoising , 2017, IEEE Transactions on Instrumentation and Measurement.

[11]  Yuesheng Xu,et al.  A B-spline approach for empirical mode decompositions , 2006, Adv. Comput. Math..

[12]  Yu Liu,et al.  Application of complete ensemble intrinsic time-scale decomposition and least-square SVM optimized using hybrid DE and PSO to fault diagnosis of diesel engines , 2017, Frontiers of Information Technology & Electronic Engineering.

[13]  Aijun Hu,et al.  Selection principle of mathematical morphological operators in vibration signal processing , 2016 .

[14]  Lijun Zhang,et al.  Multiscale morphology analysis and its application to fault diagnosis , 2008 .

[15]  Gaigai Cai,et al.  Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox , 2013 .

[16]  Andrew Ball,et al.  Modulation Sideband Separation Using the Teager–Kaiser Energy Operator for Rotor Fault Diagnostics of Induction Motors , 2019 .

[17]  Lijiang Chen,et al.  Quantum digital image processing algorithms based on quantum measurement , 2013 .

[18]  Zhe Yuan,et al.  Rolling bearing fault diagnosis based on adaptive smooth ITD and MF-DFA method , 2020 .

[19]  Dejie Yu,et al.  A gear fault diagnosis using Hilbert spectrum based on MODWPT and a comparison with EMD approach , 2009 .

[20]  Jiawei Xiang,et al.  Kernel regression residual signal-based improved intrinsic time-scale decomposition for mechanical fault detection , 2018, Measurement science and technology.

[21]  Ioannis Antoniadis,et al.  APPLICATION OF MORPHOLOGICAL OPERATORS AS ENVELOPE EXTRACTORS FOR IMPULSIVE-TYPE PERIODIC SIGNALS , 2003 .

[22]  Yu Liu,et al.  Diesel engine fault diagnosis using intrinsic time-scale decomposition and multistage Adaboost relevance vector machine , 2018 .

[23]  Zhifen Zhang,et al.  An adaptive method based on fractional empirical wavelet transform and its application in rotating machinery fault diagnosis , 2019, Measurement Science and Technology.

[24]  Heng Li,et al.  A time varying filter approach for empirical mode decomposition , 2017, Signal Process..

[25]  François Mariotti,et al.  Dose‐response analyses using restricted cubic spline functions in public health research , 2010, Statistics in medicine.

[26]  Gregory Ditzler,et al.  A Novelty Detector and Extreme Verification Latency Model for Nonstationary Environments , 2019, IEEE Transactions on Industrial Electronics.

[27]  Yonina C. Eldar.  Quantum signal processing , 2002, IEEE Signal Process. Mag..

[28]  Magdy Bayoumi,et al.  Machine Learning-Based Approach for Hardware Faults Prediction , 2020, IEEE Transactions on Circuits and Systems I: Regular Papers.

[29]  Bing Li,et al.  Bearing Fault Signal Analysis Based on an Adaptive Multiscale Combined Morphological Filter , 2020 .

[30]  Zhi Ping Fan,et al.  Improve the Envelope of EMD with Piecewise Linear Fractal Interpolation , 2010 .

[31]  Shahin Hedayati Kia,et al.  Information Fusion and Semi-Supervised Deep Learning Scheme for Diagnosing Gear Faults in Induction Machine Systems , 2019, IEEE Transactions on Industrial Electronics.

[32]  Jianbo Yu,et al.  Sparse Coding Shrinkage in Intrinsic Time-Scale Decomposition for Weak Fault Feature Extraction of Bearings , 2018, IEEE Transactions on Instrumentation and Measurement.

[33]  Miguel A. Ferrer,et al.  Application of the Teager-Kaiser energy operator in bearing fault diagnosis. , 2013, ISA transactions.

[34]  Kai Zhang,et al.  A Feature Extraction Method of Ship-Radiated Noise Based on Fluctuation-Based Dispersion Entropy and Intrinsic Time-Scale Decomposition , 2019, Entropy.

[35]  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.

[36]  Yi Chai,et al.  Gear fault diagnosis under variable conditions with intrinsic time-scale decomposition-singular value decomposition and support vector machine , 2017 .

[37]  Ling Xiang,et al.  Fault diagnosis for the gearbox of wind turbine combining ensemble intrinsic time-scale decomposition with Wigner bi-spectrum entropy , 2017 .

[38]  Kasem Khalil,et al.  Intelligent Fault-Prediction Assisted Self-Healing for Embryonic Hardware , 2020, IEEE Transactions on Biomedical Circuits and Systems.

[39]  Jingxiang Lv,et al.  Average combination difference morphological filters for fault feature extraction of bearing , 2018 .

[40]  Xiao Long Zhang,et al.  Faults diagnosis of rolling element bearings based on modified morphological method , 2011 .

[41]  Han Li,et al.  Fault diagnosis using pattern classification based on one-dimensional adaptive rank-order morphological filter , 2012 .

[42]  M. Benbouzid,et al.  EEMD-based wind turbine bearing failure detection using the generator stator current homopolar component , 2013 .

[43]  Zijian Qiao,et al.  SVD principle analysis and fault diagnosis for bearings based on the correlation coefficient , 2015 .