A novel denoising algorithm based on TVF-EMD and its application in fault classification of rotating machinery

Abstract This paper proposes a new narrow-band filtering algorithm to improve the problem of TVF-EMD algorithm decomposing too many narrow-bands. The algorithm uses the energy estimation model of IMFs combined with the energy of noise in each imf and the signal complexity evaluation standard to obtain the effectiveness operator that measures the signal content of each imf, and selects the eimf with a large effectiveness operator as the EIMFs. In this paper, three groups of rotating machine data are used for experiments. The classification accuracy of denoising signals can reach 99.98% when the effectiveness operator is accumulated to 0.9999, and the classification accuracy of the EIMFs feature matrix can reach 97.83%, which are higher than the original data control group. The algorithm only needs to deal with the advantages of EIMFs, which significantly improves the classification accuracy and iteration speed of the classifier.

[1]  Susmita Das,et al.  An efficient ECG denoising methodology using empirical mode decomposition and adaptive switching mean filter , 2018, Biomed. Signal Process. Control..

[2]  Steve McLaughlin,et al.  Development of EMD-Based Denoising Methods Inspired by Wavelet Thresholding , 2009, IEEE Transactions on Signal Processing.

[3]  Silong Peng,et al.  EMD Sifting Based on Bandwidth , 2007, IEEE Signal Processing Letters.

[4]  Ridha Ziani,et al.  Bearing fault diagnosis using multiclass support vector machines with binary particle swarm optimization and regularized Fisher’s criterion , 2017, J. Intell. Manuf..

[5]  Kun Zhang,et al.  Adaptive Kurtogram and its applications in rolling bearing fault diagnosis , 2019, Mechanical Systems and Signal Processing.

[6]  Minqiang Xu,et al.  A Review of Early Fault Diagnosis Approaches and Their Applications in Rotating Machinery , 2019, Entropy.

[7]  Ling Tang,et al.  A DBN-based resampling SVM ensemble learning paradigm for credit classification with imbalanced data , 2018, Appl. Soft Comput..

[8]  Yinsheng Chen,et al.  A Novel Rolling Bearing Fault Diagnosis and Severity Analysis Method , 2019, Applied Sciences.

[9]  Wei Chen,et al.  Performance evaluation of the GIS-based data mining techniques of best-first decision tree, random forest, and naïve Bayes tree for landslide susceptibility modeling. , 2018, The Science of the total environment.

[10]  Kaplan Kaplan,et al.  An improved feature extraction method using texture analysis with LBP for bearing fault diagnosis , 2020, Appl. Soft Comput..

[11]  Yang Yu,et al.  A fault diagnosis approach for roller bearings based on EMD method and AR model , 2006 .

[12]  Jinde Zheng,et al.  Generalized empirical mode decomposition and its applications to rolling element bearing fault diagnosis , 2013 .

[13]  Yanyang Zi,et al.  Generator bearing fault diagnosis for wind turbine via empirical wavelet transform using measured vibration signals , 2016 .

[14]  Yu Xue,et al.  Text classification based on deep belief network and softmax regression , 2016, Neural Computing and Applications.

[15]  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 .

[16]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[17]  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.

[18]  T. Balusamy,et al.  Analysis of vibration signal responses on pre induced tunnel defects in friction stir welding using wavelet transform and empirical mode decomposition , 2019 .

[19]  Satish C. Sharma,et al.  Rolling element bearing fault diagnosis using wavelet transform , 2011, Neurocomputing.

[20]  Farhat Fnaiech,et al.  Bi-spectrum based-EMD applied to the non-stationary vibration signals for bearing faults diagnosis , 2014, 2014 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR).

[21]  Changqing Shen,et al.  Fault diagnosis of rotating machines based on the EMD manifold , 2020 .

[22]  Rong Huang,et al.  Web spam classification method based on deep belief networks , 2018, Expert Syst. Appl..

[23]  Boualem Boashash,et al.  Instantaneous frequency, instantaneous bandwidth and the analysis of multicomponent signals , 1990, International Conference on Acoustics, Speech, and Signal Processing.

[24]  Han-Ping Hu,et al.  Dynamics Analysis of a New Fractional-Order Hopfield Neural Network with Delay and Its Generalized Projective Synchronization , 2018, Entropy.

[25]  Zongyan Cai,et al.  An enhanced bearing fault diagnosis method based on TVF-EMD and a high-order energy operator , 2018, Measurement Science and Technology.

[26]  Mohan S. Kankanhalli,et al.  $\mathcal{G}$ -Softmax: Improving Intraclass Compactness and Interclass Separability of Features , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[27]  Ömer Faruk Arar,et al.  A feature dependent Naive Bayes approach and its application to the software defect prediction problem , 2017, Appl. Soft Comput..

[28]  Di Peng,et al.  Fault Diagnosis of Rotating Machinery: A Review and Bibliometric Analysis , 2020, IEEE Access.

[29]  Thomas G. Habetler,et al.  Machine Learning and Deep Learning Algorithms for Bearing Fault Diagnostics - A Comprehensive Review , 2019, ArXiv.

[30]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[31]  Rabah Abdelkader,et al.  Rolling Bearing Fault Diagnosis Based on an Improved Denoising Method Using the Complete Ensemble Empirical Mode Decomposition and the Optimized Thresholding Operation , 2018, IEEE Sensors Journal.

[32]  Leonardo Franco,et al.  Layer multiplexing FPGA implementation for deep back-propagation learning , 2017, Integr. Comput. Aided Eng..

[33]  Fouzi Harrou,et al.  Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and $k$ -Nearest Neighbor Scheme , 2018, IEEE Sensors Journal.

[34]  Kaplan Kaplan,et al.  A novel feature extraction method for bearing fault classification with one dimensional ternary patterns. , 2019, ISA transactions.

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

[36]  Yaguo Lei,et al.  Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .

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

[38]  Mohan S. Kankanhalli,et al.  G-softmax: Improving Intra-class Compactness and Inter-class Separability of Features , 2019, arXiv.org.

[39]  Liang Gao,et al.  A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method , 2018, IEEE Transactions on Industrial Electronics.

[40]  Babak Hossein Khalaj,et al.  Grey Prediction Based Handoff Algorithm , 2007 .

[41]  Yaguo Lei,et al.  A review on empirical mode decomposition in fault diagnosis of rotating machinery , 2013 .

[42]  Gabriel Rilling,et al.  Empirical mode decomposition as a filter bank , 2004, IEEE Signal Processing Letters.

[43]  Feng Ding,et al.  A Fault Feature Extraction Method of Motor Bearing Using Improved LCD , 2020, IEEE Access.

[44]  Patrick J. Loughlin,et al.  Modified Cohen-Lee time-frequency distributions and instantaneous bandwidth of multicomponent signals , 2001, IEEE Trans. Signal Process..

[45]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[46]  I. Johnstone,et al.  Ideal spatial adaptation by wavelet shrinkage , 1994 .

[47]  J. Rafiee,et al.  Application of mother wavelet functions for automatic gear and bearing fault diagnosis , 2010, Expert Syst. Appl..

[48]  Siva Ramakrishna Madeti,et al.  Modeling of PV system based on experimental data for fault detection using kNN method , 2018, Solar Energy.