Abstract
Piezoelectric ceramic cracking (PCC) is the main reason leading to failure of ultrasonic motors. To solve the problem that the fault information is too weak to reflect the cracking condition especially in the early degradation stage, a degradation state identification method based on morphological boundary span coverage statistics was proposed in this paper. Firstly, the average morphological cover area of standard data was adopted as the degradation feature for PCC. Then standard degradation state rectangles (SDSRs) were constructed based on the degradation feature. The height of SDSR was optimized to improve the classification accuracy via training data with the help of genetic algorithm. Lastly, the coverage statistics obtained by the relationship between test data’s morphological boundary span signal and the constructed SDSR can be taken as a fair indicator for the actual degradation state. The experimental results show that this method is feasible and effective, and could achieve a satisfying performance to identify the different degradation states of PCC.
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References
D.A. Stepanenko, V.T. Minchenya, Development and study of novel non-contact ultrasonic motor based on principle of structural asymmetry. Ultrasonics 52(7), 866–872 (2012)
X. Li, Z. Yao, Q. Lv, Z. Liu, Dynamic modeling and characteristics analysis of a modal-independent linear ultrasonic motor. Ultrasonics 72, 117–127 (2016)
Y. Zhou, T.-W. Kim, A moving thermal dielectric crack in piezoelectric ceramics with a shearing force applied on its surface. Appl. Math. Model. 63(11), 1–17 (2018)
O. Viun, A. Komarov, Y. Lapusta, V. Loboda, A polling direction influence on fracture parameters of a limited permeable interface crack in a piezoelectric bi-material. Eng. Fract. Mech. 191(15), 143–152 (2018)
Y.F. Zhao, Y.G. Guo, T.C. Miao et al., An iterative approach for analyzing cracks in two-dimensional piezoelectric media with exact boundary conditions. Eng. Anal. Bound. Elem. 90(5), 76–85 (2018)
Z.W. Wang, Q.H. Zhang, J.B. Xiong et al., Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests. IEEE Sens. J. 17, 5581–5588 (2017)
L.Y. Zhao, W. Yu, R.Q. Yan, Gearbox fault diagnosis using complementary ensemble empirical mode decomposition and permutation entropy. Shock Vib. 2016, 1–8 (2016)
J. Sun, H. Li, B. Xu, The morphological undecimated wavelet decomposition—discrete cosine transform composite spectrum fusion algorithm and its application on hydraulic pumps. Measurement 94, 794–805 (2016)
T.K. Gong, Y.B. Yuan, X.H. Yuan et al., Application of optimized multiscale mathematical morphology for bearing fault diagnosis. Meas. Sci. Technol. 28(4), 1–15 (2017)
B. Li, P.L. Zhang, Z.J. Wang et al., Gear fault detection using multi-scale morphological filters. Measurement 44(10), 2078–2089 (2011)
Y.B. Li, G.Y. Li, Y.T. Yang et al., A fault diagnosis scheme for planetary gearboxes using adaptive multi-scale morphology filter and modified hierarchical permutation entropy. Mech. Syst. Signal Process. 105, 319–337 (2018)
A.J. Hu, L. Xiang, An optimal selection method for morphological filter’s parameters and its application in bearing fault diagnosis. J. Mech. Sci. Technol. 30(3), 1055–1063 (2016)
B. Wang, X. Hu, H.R. Li, Rolling bearing performance degradation condition recognition based on mathematical morphological fractal dimension and fuzzy C-means. Measurement 109, 1–8 (2017)
Z. Zheng, W.L. Jiang, Z.W. Wang et al., Gear fault diagnosis method based on local mean decomposition and generalized morphological fractal dimensions. Mech. Mach. Theory 91, 151–167 (2015)
H. Yu, H.R. Li, B.H. Xu, Rolling bearing degradation state identification based on LCD relative spectral entropy. J. Fail. Anal. Prev. 16(4), 655–666 (2016)
X.M. Jiang, L. Wu, L.W. Pan et al., Rolling bearing fault diagnosis based on ELCD permutation entropy and RVM. J. Eng. 2016, 1–7 (2016)
S. Zhou, S. Qian, W. Chang et al., A novel bearing multi-fault diagnosis approach based on weighted permutation entropy and an improved SVM ensemble classifier. Sensors 18(6), 1934 (2018)
Y.J. Li, W.H. Zhang, Q. Xiong et al., A rolling bearing fault diagnosis strategy based on improved multiscale permutation entropy and least squares SVM. J. Mech. Sci. Technol. 31(6), 2711–2722 (2017)
Y.B. Li, X.H. Liang, Y.T. Yang et al., Early fault diagnosis of rotating machinery by combining differential rational spline-based LMD and K-L divergence. IEEE Trans. Instrum. Meas. 66(11), 3077–3090 (2017)
F. Giantomassi, S. Larlori et al., Electric motor fault detection and diagnosis by kernel density estimation and Kullback–Leibler divergence based on stator current measurements. IEEE Trans. Ind. Electron. 62(3), 1770–1780 (2015)
Z.K. Tian, H.R. Li, H.Q. Gu et al., Degradation status identification of a hydraulic pump based on local characteristic-scale decomposition and JRD. J. Vib. Shock 35(20), 54–59 (2016)
J. Serra, Image Analysis and Mathematical Morphology (Academic Press, New York, 1982)
J.M. Alliot, H. Gruber, G. Joly G et al., Genetic algorithms for solving air traffic control conflicts, in Proceedings of 9th IEEE Conference on Artificial Intelligence for Applications. IEEE Computer Society (1993)
Acknowledgments
This project is supported by the National Natural Science Foundation of China (Grant No. 51877070), China Postdoctoral Science Foundation (Grant No. 2017M623404), the Natural Science Youth Foundation of Hebei (Grant No. E2017208086) and the Science and Technology Research Youth Foundation for Hebei College (Grant No. QN2017329).
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An, G., Li, R., Song, K. et al. Degradation State Identification for Ceramic in Ultrasonic Motor Based on Morphological Boundary Span Analysis. J Fail. Anal. and Preven. 19, 761–770 (2019). https://doi.org/10.1007/s11668-019-00659-1
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DOI: https://doi.org/10.1007/s11668-019-00659-1