In the field of system health management, the quality of rolling equipment is very important. Therefore, the fault diagnosis of rolling bearings has become a hot research topic. In this paper, the traditional fault feature extraction method is used to optimize the non-linear and non-stationary characteristics of the bearing vibration signal. Furthermore, in order to improve the performance of the fault diagnosis, a novel signal fingerprint is proposed to recognize the fault type. The simulation result show that the new method is successful and effective, and the recognition rate can be improved up to 95.33%, which is better than the traditional methods.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Randall RB, Antoni J (2011) Rolling element bearing diagnosticsa tutorial. Mech Syst Signal Process 25(2):485–520
Dou Z, Xu X, Lin Y, Zhou R (2014) Application of ds evidence fusion method in the fault detection of temperature sensor. Mathematical Problems in Engineering 2014
J. Gertler (2017). Fault detection and diagnosis in engineering systems. Routledge
Brkovic A, Gajic D, Gligorijevic J, Savic-Gajic I, Georgieva O, Di Gennaro S (2017) Early fault detection and diagnosis in bearings for more efficient operation of rotating machinery. Energy 136:63–71
Abbaspour A, Aboutalebi P, Yen KK, Sargolzaei A (2017) Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: application in uav. ISA Trans 67:317–329
Khalastchi E, Kalech M (2018) On fault detection and diagnosis inrobotic systems. ACM Computing Surveys(CSUR) 51(1):1–24
Peeters C, Guillaume P, Helsen J (2018) Vibration-based bearing fault detection for operations and maintenance cost reduction in wind energy. Renew Energy 116:74–87
Tandon N, Choudhury A (1999) A review of vibration and acoustic measurement methods for the detection of defects in rolling element bearings. Tribol Int 32(8):469–480
Zhang X, Zhou J (2013) Multi-fault diagnosis for rolling element bearings based on ensemble empirical mode decomposition and optimized support vector machines. Mech Syst Signal Process 41(1–2):127–140
Liu S, Bai W, Liu G, Li W, Srivastava HM (2018) Parallel fractal compression method for big video data. Complexity, vol 2018
Wang M, Li Z, Huang D, Guo X (2018) Performance analysis of information fusion method based on bell function. International Journal of Performability Engineering 14(4):729–740
Chen Z, Ding SX, Peng T, Yang C, Gui W (2017) Fault detection for non-gaussian processes using generalized canonical correlation analysis and randomized algorithms. IEEE Trans Ind Electron 65(2):1559–1567
Yu L, Junhong Z, Fengrong B, Jiewei L, Wenpeng M (2014) A fault diagnosis approach for diesel engine valve train based on improved itd and sdag-rvm. Measurement Science and Technology 26(2):025003
Su Z, Tang B, Liu Z, Qin Y (2015) Multi-fault diagnosis for rotating machinery based on orthogonal supervised linear local tangent space alignment and least square support vector machine. Neurocomputing 157:208–222
El Morsy M, Achtenova G (2015) Rolling bearing fault diagnosis techniques-autocorrelation and cepstrum analyses. In: 2015 23rd Mediterranean Conference on Control and Automation(MED), pp.328–334. IEEE
Huang H, Ouyang H, Gao H, Guo L, Li D, Wen J (2016) A feature extraction method for vibration signal of bearing incipient degradation. Measurement Science Review 16(3):149–159
Lin Y, Li Y, Yin X, Dou Z (2018) Multisensor fault diagnosis modeling based on the evidence theory. IEEE Trans Reliab 67(2):513–521
Peng Z, Chu F (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18(2):199–221
Weickert T, Benjaminsen C, Kiencke U (2008) Analytic wavelet packetscombining the dual-tree approach with wavelet packets for signal analysis and filtering. IEEE Trans Signal Process 57(2):493–502
Žvokeli M, Zupan S, Prebil I (2011) Non-linear multivariate and multiscale monitoring and signal denoising strategy using kernel principal component analysis combined with ensemble empirical mode decomposition method. Mech Syst Signal Process 25(7):2631–2653
Delgado-Arredondo PA, Morinigo-Sotelo D, Osornio-Rios RA, Avina-Cervantes JG, Rostro-Gonzalez H, de Jesus RomeroTroncoso R (2017) Methodology for fault detection in induction motors via sound and vibration signals. Mech Syst Signal Process 83:568–589
Imaouchen Y, Kedadouche M, Alkama R, Thomas M (2017) A frequency-weighted energy operator and complementary ensemble empirical mode decomposition for bearing fault detection. Mech Syst Signal Process 82:103–116
Lu S, He Q, Wang J (2019) A review of stochastic resonance in rotatingmachine fault detection. Mech Syst Signal Process 116:230–260
Qiao Z, Lei Y, Li N (2019) Applications of stochastic resonance to machinery fault detection: a review and tutorial. Mech Syst Signal Process 122:502–536
Liu S, Pan Z, Fu W, Cheng X (2017) Fractal generation method based on asymptote family of generalized Mandelbrot set and its application. Journal of Nonlinear Sciences and Applications 3:1148–1161
Zheng S, Qi P, Chen S, Yang X (2019) Fusion methods for cnn-based automatic modulation classification. IEEE Access 7:66496–66504
Luo B, Wang H, Liu H, Li B, Peng F (2018) Early fault detection of machine tools based on deep learning and dynamic identification. IEEE Trans Ind Electron 66(1):509–518
Zidi S, Moulahi T, Alaya B (2017) Fault detection in wireless sensor networks through svm classifier. IEEE Sensors J 18(1):340–347
Zhou JT, Du J, Zhu H, Peng X, Liu Y, Goh RSM (2019) Anomalynet: an anomaly detection network for video surveillance. IEEE Transactions on Information Forensics and Security 14(10):2537–2550
Xiao Y, Xing C, Zhang T, Zhao Z (2019) An intrusion detection model based on feature reduction and convolutional neural networks. IEEE Access 7:42210–42219
Cobb WE, Garcia EW, Temple MA, Baldwin RO, Kim YC (2010) Physical layer identification of embedded devices using rf-dna fingerprinting. In: 2010-Milcom 2010 Military Communications Conference, pp.2168–2173. IEEE
Y. Lin, M. Wang, X. Zhou, G. Ding, and S. Mao,” (2020). Dynamic spectrum interaction of uav flight formation communication with priority:A deep reinforcement learning approach,” IEEE Transactions on Cognitive Communications and Networking
J. Li and Y. Ying (2020). “Gas turbine gas path diagnosis under transient operating conditions: A steady state performance model based localoptimization approach,” Appl Therm Eng, p.115025
G. Baldini, R. Giuliani, G. Steri, and R. Neisse (2017), “Physical layer authentication of internet of things wireless devices through permutation and dispersion entropy,” in 2017 Global Internet of Things Summit(GloTS), pp.1–6, IEEE
Ureten O, Serinken N (2007) Wireless security through rf fingerprinting. Can J Electr Comput Eng 32(1):27–33
T. J. Carbino, M. A. Temple, and J. Lopez (2015), “A comparison of phy-based fingerprinting methods used to enhance network access control,” in IFIP International Information Security and Privacy Conference, pp.204–217. Springer
Peng L, Hu A, Zhang J, Jiang Y, Yu J, Yan Y (2018) Design of a hybrid rf fingerprint extraction and device classification scheme. IEEE Internet Things J 6(1):349–360
Wang H, Li J, Guo L, Dou Z, Lin Y, Zhou R (2017) Fractal complexity-based feature extraction algorithm of communication signals. Fractals 25(04):1740008
Klein RW, Temple MA, Mendenhall MJ (2009) Application of wavelet-based rf fingerprinting to enhance wireless network security. J Commun Netw 11(6):544–555
Huang G, Yuan Y, Wang X, Huang Z (2016) Specific emitter identification based on nonlinear dynamical characteristics. Can J Electr Comput Eng 39(1):34–41
Lin Y, Zhu X, Zheng Z, Dou Z, Zhou R (2019) The individual identification method of wireless device based on dimensionality reduction and machine learning. J Supercomput 75(6):3010–3027
Whitney AW (1971) A direct method of nonparametric measurement selection. IEEE Trans Comput 100(9):1100–1103
Bearing Data Center Website, Case Western Reserve University n.d., https://csegroups.case.edu/bearingdatacenter/home
Conflict of interest
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
This work is supported by the National Natural Science Foundation of China (Grant No.51909125), Zhejiang Province Public Welfare Technology Application Research Project (No.LGF20E060001) and the K.C. Wong Magna Fund in Ningbo University.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Shi, F., Xu, G. Research on Fault Feature Extraction and Recognition of Rolling Bearings. Mobile Netw Appl (2020). https://doi.org/10.1007/s11036-020-01611-6
- Fault diagnosis
- Feature extraction
- Feature selection
- Fault recognition
- Signal fingerprint