Rolling bearing fault diagnosis and health assessment using EEMD and the adjustment Mahalanobis–Taguchi system

References

• Cudney, E. A., Paryani, K., & Ragsdell, K. M. (2006). Applying the Mahalanobis-Taguchi system to vehicle ride. Concurrent Engineering Research & Applications, 1(3), 251–259.
   Google Scholar
• Guo, W., & Tse, P. W. (2013). A novel signal compression method based on optimal ensemble empirical mode decomposition for bearing vibration signals. Journal of Sound and Vibration, 332(2), 423–441.
   Web of Science ®Google Scholar
• Hu, J., Zhang, L., Liang, W., & Wang Z. (2009). Incipient mechanical fault detection based on multifractal and MTS methods. Petroleum Science, 6(2), 208–216.
   Web of Science ®Google Scholar
• Huang, Y., Liu, C., Zha, X. F., & Li Y. (2010). Research article: A lean model for performance assessment of machinery using second generation wavelet packet transform and Fisher criterion[J]. Expert Systems with Applications An International Journal, 37(5), 3815–3822.
   Web of Science ®Google Scholar
• Huang, Y., Wu, D., Zhang, Z., Chen H., & Chen S. (2017). EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM. Journal of Materials Processing Technology, 239, 92–102.
   Web of Science ®Google Scholar
• Huang, Z. S. S. R. N. E. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454, 903–995.
   Web of Science ®Google Scholar
• Jin, G., Matthews, D. E., & Zhou, Z. (2013). A Bayesian framework for on-line degradation assessment and residual life prediction of secondary batteries inspacecraft. Reliability Engineering & System Safety, 113(1), 7–20.
   Web of Science ®Google Scholar
• Lee, Y. C., & Teng, H. L. (2009). Predicting the financial crisis by Mahalanobis-Taguchi system - examples of Taiwan's electronic sector. Expert Systems with Applications, 36(4), 7469–7478.
   Web of Science ®Google Scholar
• Li, G., Li, J., Wang, S., & Chen X. (2016). Quantitative evaluation on the performance and feature enhancement of stochastic resonance for bearing fault diagnosis. Mechanical Systems and Signal Processing, 81, 108–125.
   Web of Science ®Google Scholar
• Li, Y., Xu, M., Wang, R., & Huang W. (2016). A fault diagnosis scheme for rolling bearing based on local mean decomposition and improved multiscale fuzzy entropy. Journal of Sound and Vibration, 360, 277–299.
   Web of Science ®Google Scholar
• Lv, Z., Tang, B., Zhou, Y., & Zhou C. (2016). A novel method for mechanical fault diagnosis based on variational mode decomposition and multikernel support vector machine. Shock and Vibration, 2016(5), 1–11.
   Web of Science ®Google Scholar
• Ngaopitakkul, A., & Bunjongjit, S. (2013). An application of a discrete wavelet transforms and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line. International Journal of Systems Science, 44(9), 1745–1761.
   Web of Science ®Google Scholar
• Pal, A., & Maiti, J. (2010). Development of a hybrid methodology for dimensionality reduction in Mahalanobis–Taguchi system using Mahalanobis distance and binary particle swarm optimization. Expert systems with applications, 37(2), 1286–1293.
   Web of Science ®Google Scholar
• Pan, P. Y., Cheng, K., & Harrison, D. K. (2002). Development of an internet-based intelligent design support system for rolling element bearings. International Journal of Systems Science, 33(6), 403–419.
   Web of Science ®Google Scholar
• Patton, R. J., Chen, J., & Chen, J. (2000). A study on neuro-fuzzy systems for fault diagnosis. International Journal of Systems Science, 31(11), 1441–1448.
   Web of Science ®Google Scholar
• Shakya, P., Kulkarni, M. S., & Darpe, A. K. (2015). Bearing diagnosis based on Mahalanobis–Taguchi–Gram–Schmidt method. Journal of Sound and Vibration, 337, 342–362.
   Web of Science ®Google Scholar
• Sharma, A., & Kankar, MAP. (2014). Feature extraction and fault severity classification in ball bearings. Journal of Vibration & Control, 22(1), 176–192.
   Web of Science ®Google Scholar
• Shi, H., Liu, J., Wu, Y., Zhang, K., Zhang, L., & Xue, P. (2014). Fault diagnosis of nonlinear and large-scale processes using novel modified kernel Fisher discriminant analysis approach. International Journal of Systems Science, 40(9), 1095–1109.
   Google Scholar
• Soylemezoglu, A., Jagannathan, S., & Saygin, C. (2010). Mahalanobis Taguchi System (MTS) as a prognostics tool for rolling element bearing failures. Journal of Manufacturing Science & Engineering, 132(5), 051014.
   Web of Science ®Google Scholar
• Taguchi, G., & Jugulum, R. (2002). The Mahalanobis-Taguchi strategy: A pattern technology system[M]. New York, NY: John Wiley & Sons.
   Google Scholar
• Tse, P. W., & Wang, D. (2015). Enhancing the abilities in assessing slurry pumps' performance degradation and estimating their remaining useful lives by using captured vibration signals. Journal of Vibration & Control. 23(12), 1925–1937.
   Web of Science ®Google Scholar
• Vakharia, V., Gupta, V. K., & Kankar, P. K. (2014). A multiscale entropy based approach to select wavelet for fault diagnosis of ball bearings. Journal of Vibration and Control, 21(16), 1–9.
   Web of Science ®Google Scholar
• Wang, W., & Pecht, M. (2011). Economic analysis of canary-based prognostics and health management. IEEE Transactions on Industrial Electronics, 58(7), 3077–3089.
   Web of Science ®Google Scholar
• Wang, Z., Lu, C., & Wang, Z., Liu, H., & Fan, H., (2013). Fault diagnosis and health assessment for bearings using the Mahalanobis-Taguchi system based on EMD-SVD[J]. Transactions of the Institute of Measurement and Control, 35(6), 798–807.
   Web of Science ®Google Scholar
• Widodo, A., & Yang, B. S. (2011). Application of relevance vector machine and survival probability to machine degradation assessment. Expert Systems with Applications, 38(3), 2592–2599.
   Web of Science ®Google Scholar
• Woodall, W. H., Koudelik Rachelle, & Tsui Kwok-Leung, (2003). A review and analysis of the Mahalanobis-Taguchi system. Technometrics, 45, 1–15.
   Web of Science ®Google Scholar
• Xu, J., Wang, Y., & Xu, L. (2014). PHM-Oriented integrated fusion prognostics for aircraft engines based on sensor data[J]. IEEE Sensors Journal, 14(4), 1124–1132.
   Web of Science ®Google Scholar
• Miao, Q., Tang, C., Liang, W., & Pecht, M. (2013). Health assessment of cooling fan bearings using wavelet-based filtering. Sensors. 13(1), 274–291.
   Google Scholar
• Jia, X., Jin, C., Buzza, M., Wang, W., & Lee, J. (2016). Wind turbine performance health assessment based on a novel similarity metric for machine performance curves, 99, Renew Energ, 1191–1201.
   Google Scholar