Rolling bearing fault diagnosis based on health baseline method
Tong Zhang1, Chen Lu2, Laifa Tao3, Kun Li4
1, 4School of Reliability and Systems Engineering, Beihang University, Beijing, 100191, China
2, 3Science and Technology on
Reliability and Environmental Engineering Laboratory,
3School of Aeronautic Science and Engineering, Beihang University, Beijing, 100191, China
E-mail: email@example.com, firstname.lastname@example.org, email@example.com, firstname.lastname@example.org
Abstract. In order to excavate the relationship between the different features of the vibration signal, and to provide more useful information for the fault diagnosis of rolling bearings, this paper developed a new method of fault diagnosis-health baseline method and introduced the technological process of this method in detail. Through the case study, a health baseline based on two kinds of linear models was constructed. After testing, this method can distinguish the normal state of the rolling bearing, the external ring fault and the rolling element fault, which indicates that the method was feasible and effective for the fault diagnosis of the rolling bearing.
Keywords: rolling bearing, fault diagnosis, health baseline, association relationship.
 Li B., Chow M. Y., Tipsuwan Y., et al. Neural-network-based motor rolling bearing fault diagnosis. IEEE Transactions on Industrial Electronics, Vol. 47, Issue 5, 2000, p. 1060‑1069.
 Shuang L. Fault pattern recognition of rolling bearing based on singularity value decomposition and support vector machine. Transactions of the Chinese Society of Agricultural Engineering, 2007.
 Randall R. B., Antoni J. Rolling element bearing diagnostics a tutorial. Mechanical Systems and Signal Processing, Vol. 25, Issue 2, 2011, p. 485‑520.
 Tian Y., Ma J., Lu C., et al. Rolling bearing fault diagnosis under variable conditions using LMD‑SVD and extreme learning machine. Mechanism and Machine Theory, Vol. 90, 2015, p. 175‑186.
 Yi C., Lu Y., Dang Z., et al. Quaternion singular spectrum analysis using convex optimization and its application to fault diagnosis of rolling bearing. Measurement, Vol. 103, 2017, p. 321‑332.
 Hu H., Wen Y., Chua T. S., et al. Toward scalable systems for big data analytics: a technology tutorial. IEEE Access, Vol. 2, Issue 1, 2017, p. 652‑687.
 Ziegel E. R. Fault Detection and Diagnosis in Industrial Systems. Advanced Textbooks in Control and Signal Processing, Vol. 12, Issue 3, 2002, p. 453‑454.
 Mutawa N. A., Baggili I., Marrington A. Forensic analysis of social networking applications on mobile devices. Digital Investigation, Vol. 9, Issue 15, 2012, p. 24‑33.
 Trawiński B., Smętek M., Telec Z., et al. Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms. International Journal of Applied Mathematics and Computer Science, Vol. 22, Issue 4, 2012, p. 449‑471.
 Preacher K. J., Hayes A. F. SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods Instruments and Computers, Vol. 36, Issue 4, 2004, p. 717‑731.
 Aflori C., Craus M. Grid implementation of the apriori algorithm. Advances in Engineering Software, Vol. 38, Issue 5, 2007, p. 295‑300.
Cite this article
Zhang Tong, Lu Chen, Tao Laifa, Li Kun Rolling bearing fault diagnosis based on health baseline method. Vibroengineering PROCEDIA, Vol. 14, 2017, p. 141‑145.
© JVE International Ltd. Vibroengineering PROCEDIA. Oct 2017, Vol. 14. ISSN 2345-0533