Local and Global SR for Bearing Sensor-based Vibration Signal Classification

doi:10.23940/ijpe.19.10.p11.26572666

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (10): 2657-2666.doi: 10.23940/ijpe.19.10.p11.26572666

• Orginal Article • Previous Articles Next Articles

Local and Global SR for Bearing Sensor-based Vibration Signal Classification

Shaohui Zhang^a, Man Wang^a, Canyi Du^b,*, and Edgar Estupinan^c

^aSchool of Mechanical Engineering, Dongguan University of Technology, Dongguan, 523808, China ^bSchool of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou, 510665, China
^cDepartment of Mechanical Engineering, University of Tarapacá, Arica, Chile

Submitted on ; Revised on ; Accepted on
About author:* Corresponding author. <i>E-mail address</i>: 229457893@qq.com
Supported by:
This work was supported by the Natural Science Foundation of China (No 51605406, 51605405), Education and Scientific Research Project for Young and Middle-aged Teachers of Fujian (No JAT170413), Natural Science Foundation of Fujian Province (No 2018J01531), Research Start-up Funds of DGUT (No GC300501-26), and Postdoctoral Science Foundation of China (No 2019M652881)

Abstract

Abstract:

Spectral regression (SR) is a method of feature extraction that realizes dimension reduction by the least squares method and can avoid eigen-decomposition of dense matrices. However, it only considers the affinity graph and misses the global information. In this paper, a novel feature extraction algorithm, called local and global spectral regression (LGSR), is proposed and applied to extract fault features from frequency-domain and time-domain features of vibration signals of bearing sensors. LGSR, which is the development of SR, is able to discover both local and global information of data manifold. Compared with other similar approaches (such as NPE, PCA, and SR), experiments of bearing defect classification validate that LGSR shows better ability to extract identity information for machine defect classification.

Key words: spectral regression, sensor-based signals, feature extraction, conditions classification

Shaohui Zhang, Man Wang, Canyi Du, and Edgar Estupinan. Local and Global SR for Bearing Sensor-based Vibration Signal Classification [J]. Int J Performability Eng, 2019, 15(10): 2657-2666.

Add to citation manager EndNote|Reference Manager|ProCite|BibTeX|RefWorks

References 17.

1.	S. Dong, Z. Wu, Y. Pan, H. Su,Y. Liu, “Hidden-Markov-Model-based Asynchronous Filter Design of Nonlinear Markov Jump Systems in Continuous-Time Domain,” IEEE Transactions on Cybernetics, Vol. 49, No. 6, pp. 2294-2304, 2018
2.	L. Wang, Y. Shao,Z. Cao, “Optimal Demodulation Subband Selection for Sun Gear Crack Fault Diagnosis in Planetary Gearbox,”Measurement, Vol. 125, pp. 554-563, 2018
3.	D. Wang, Y. Zhao, C. Yi, K. L. Tsui,J. H. Lin, “Sparsity Guided Empirical Wavelet Transform for Fault Diagnosis of Rolling Element Bearings,”Mechanical Systems and Signal Processing, Vol. 101, pp. 292-308, 2018
4.	W. J. Staszewski, K. Worden,G. R. Tomlinson, “Time-Frequency Analysis in Gearbox Fault Detection using the Wigner-Ville Distribution and Pattern Recognition,”Mechanical Systems and Signal Processing, Vol. 11, pp. 673-692, 1997
5.	Y. Aït-Sahalia and D. Xiu, “Principal Component Analysis of High-Frequency Data,” Journal of the American Statistical Association, Vol. 114, No. 525, pp. 287-303, 2019
6.	R. O.Duda and P. E. Hart, “Pattern Classification and Scene Analysis,” John Wiley and Sons, New York, 1973
7.	R. Q.Yan and R. X. Gao, “Approximate Entropy as a Diagnostic Tool for Machine Health Monitoring,”Mechanical Systems and Signal Processing, Vol. 21, pp. 824-839, 2007
8.	J. B. Yu, “Local and Nonlocal Preserving Projection for Bearing Defect Classification and Performance Assessment,”IEEE Transactions on Industrial Electronics, Vol. 59, pp. 2363-2376, 2012
9.	Y. Wang, G. Xu, L. Liang,K. Jiang, “Detection of Weak Transient Signals based on Wavelet Packet Transform and Manifold Learning for Rolling Element Bearing Fault Diagnosis,”Mechanical Systems and Signal Processing, Vol. 54, pp. 259-276, 2015
10.	X. Ding, Q. Li, L. Lin, Q. He,Y. Shao, “Fast Time-Frequency Manifold Learning and Its Reconstruction for Transient Feature Extraction in Rotating Machinery Fault Diagnosis,”Measurement, Vol. 141, pp. 380-395, 2019
11.	X. Ding, Q. He, Y. Shao,W. Huang, “Transient Feature Extraction based on Time-Frequency Manifold Image Synthesis for Machinery Fault Diagnosis,” IEEE Transactions on Instrumentation and Measurement, Vol. 68, No. 11, pp. 4242-4252, 2019
12.	D. Cai, “Spectral Regression: A Regression Framework for Efficient Regularized Subspace Learning,” Ph.D. Thesis, University of Illinois at Urbana-Champaign, Urbana, IL, USA, 2009
13.	Z. G. Xia, S. X. Xia, L. Wan,S, Y. Cai, “Spectral Regression based Fault Feature Extraction for Bearing Accelerometer Sensor Signals,”Sensors, Vol. 12, pp. 13694-13719, 2012
14.	W. H. Li, T. L. Shi, G. L. Liao,S. Z. Yang, “Feature Extraction and Classification of Gear Faults using Principal Component Analysis,”Journal of Quality in Maintenance Engineering, Vol. 9, pp. 132-143, 2003
15.	P. Tse and D. Atherton, “Prediction of Machine Deterioration using Vibration based Fault Trends and Recurrent Neural Networks,”Journal of Vibration and Acoustics-Transactions of the ASME, Vol. 121, pp. 355-362, 1999
16.	K. Ding, W. Li,X. Zhu, “Practical Technologies for Gear and Gearbox Fault Diagnosis,” China Machine Press, Beijing, August, 2005
17.	K. A. Loparo, et al., “Bearingdata Center, Case Western Reserve University,”(http://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website, 2009)

[1]	P. Antony Seba and J. V. Bibal Benifa. Hybrid Outlier Detection Strategy and Weighted Decision Matrix Ordinal Classifier for CKD Severity Prediction [J]. Int J Performability Eng, 2023, 19(2): 144-154.
[2]	Sagnik Pal, Rutvik Patel, Vijayasherly V., and Ramani Selvanambi. Hashtag Recommendation System for Instagram Posts using Transfer Learning with EfficientNet and ALS Model [J]. Int J Performability Eng, 2022, 18(8): 552-558.
[3]	Roop Preet Kaur, Anshu Sharma, Inderpal Singh, and Rahul Malhotra. Deep Learning-Based Pneumonia Recognition from Chest X-Ray Images [J]. Int J Performability Eng, 2022, 18(5): 380-386.
[4]	Palak Girdhar, Prashant Johri, and Deepali Virmani. An Improved Empirical Hyper-Parameter Tuned Supervised Model for Human Activity Recognition based on Motion Flow and Deep Learning [J]. Int J Performability Eng, 2022, 18(11): 808-816.
[5]	D. Bhavana, K. Kishore Kumar, Medasani Bipin Chandra, P.V. Sai Krishna Bhargav, D. Joy Sanjanaa, and G. Mohan Gopi. Hand Sign Recognition using CNN [J]. Int J Performability Eng, 2021, 17(3): 314-321.
[6]	Narayani Patil, Kalyani Ingole, and T. Rajani Mangala. Deep Convolutional Neural Networks Approach for Classification of Lung Diseases using X-Rays: COVID-19, Pneumonia, and Tuberculosis [J]. Int J Performability Eng, 2020, 16(9): 1332-1340.
[7]	Bo Dan, Shan Gao, and Zhihong Ji. Ship Target Recognition Technology of Radar High Resolution Range Profile based on Machine Learning [J]. Int J Performability Eng, 2020, 16(4): 537-548.
[8]	Shuang Liu, Xing Cui, Jiayi Li, Hui Yang, and Niko Lukač. Pedestrian Detection based on Faster R-CNN [J]. Int J Performability Eng, 2019, 15(7): 1792-1801.
[9]	Xin Zhang, Jianmin Zhao, Xianglong Ni, Haiping Li, and Fucheng Sun. Degradation Index Extraction and Degradation Trend Prediction for Rolling Bearing [J]. Int J Performability Eng, 2019, 15(5): 1334-1342.
[10]	Xinliang Wang, Zhigang Guo, Jianlin Chen, Na Liu, and Wei Fang. Detection Algorithm of Friction and Wear State of Large Mechanical and Electrical Equipment in Coal Mine based on C-SVC [J]. Int J Performability Eng, 2019, 15(3): 813-821.
[11]	Yanan Liu and Xinghao Guo. Modulation Recognition based on Wavelet Transform and Fractal Theory [J]. Int J Performability Eng, 2019, 15(3): 998-1004.
[12]	Yanhua Wang, Yaqiu Liu, and Weipeng Jing. Hadoop-based Parallel Algorithm for Data Mining in Remote Sensing Images [J]. Int J Performability Eng, 2019, 15(11): 2860-2870.
[13]	Cheng Peng, Jing He, Hao Chi, Xinpan Yuan, and Xiaojun Deng. Icing Prediction of Fan Blade based on a Hybrid Model [J]. Int J Performability Eng, 2019, 15(11): 2882-2890.
[14]	Wei Huang, Xiao Dong, Wenqian Shang, Weiguo Lin, and Menghan Yan. An Improved Optimal Method for Classification Problem [J]. Int J Performability Eng, 2019, 15(11): 3031-3041.
[15]	Shaohua Yang, Guoliang Lu, Aiqun Wang, and Peng Yan. High-Level Feature Extraction based on Correlogram for State Monitoring of Rotating Machinery with Vibration Signals [J]. Int J Performability Eng, 2019, 15(1): 220-229.

Local and Global SR for Bearing Sensor-based Vibration Signal Classification

PDF

Knowledge

Abstract

Cite this article

share this article

References 17.

Related Articles 15

Recommended 0