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A Novel Three-stage Feature Fusion Methodology and its Application in Degradation State Identification for Hydraulic Pumps


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[1] Du, J., Wang, S., Zhang, H. (2013). Layered clustering multi-fault diagnosis for hydraulic piston pump. Mechanical Systems and Signal Processing, 36 (2), 487–504.10.1016/j.ymssp.2012.10.020 Search in Google Scholar

[2] Yu, H., Li, H., Li, Y. (2020). Vibration signal fusion using improved empirical wavelet transform and variance contribution rate for weak fault detection of hydraulic pumps. ISA Transactions, 107, 385–401.10.1016/j.isatra.2020.07.02532768135 Search in Google Scholar

[3] Sun, J., Li, H., Xu, B. (2016). The morphological undecimated wavelet decomposition–discrete cosine transform composite spectrum fusion algorithm and its application on hydraulic pumps. Measurement, 94, 794–805.10.1016/j.measurement.2016.09.024 Search in Google Scholar

[4] Zhong, K., Han, M., Han, B. (2019). Data-driven based fault prognosis for industrial systems: A concise overview. IEEE/CAA Journal of Automatica Sinica, 7 (2), 330–345. Search in Google Scholar

[5] Xu, Y., Tian, W., Zhang, K., Ma, C. (2019). Application of an enhanced fast kurtogram based on empirical wavelet transform for bearing fault diagnosis. Measurement Science and Technology, 30 (3), 035001.10.1088/1361-6501/aafb44 Search in Google Scholar

[6] Li, H., Tian, Z., Yu, H., Xu, B. (2019). Fault prognosis of hydraulic pump based on bispectrum entropy and deep belief network. Measurement Science Review, 19 (5), 195–203.10.2478/msr-2019-0025 Search in Google Scholar

[7] Kaya, Y., Kuncan, M., Kaplan, K., Minaz, M.R., Ertunc, H.M. (2020). Classification of bearing vibration speeds under 1D-LBP based on eight local directional filters. Soft Computing, 24 (16), 12175–12186.10.1007/s00500-019-04656-2 Search in Google Scholar

[8] Kaya, Y., Kuncan, M., Kaplan, K., Minaz, M.R., Ertunc, H.M. (2021). A new feature extraction approach based on one dimensional gray level co-occurrence matrices for bearing fault classification. Journal of Experimental & Theoretical Artificial Intelligence, 33 (1), 161–178.10.1080/0952813X.2020.1735530 Search in Google Scholar

[9] Bayram, S., Kaplan, K., Kuncan, M., Ertunc, H.M. (2014). The effect of bearings faults to coefficients obtaned by using wavelet transform. In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 991–994. ISBN 978-1-4799-4874-1.10.1109/SIU.2014.6830398 Search in Google Scholar

[10] Kuncan, M. (2020). An intelligent approach for bearing fault diagnosis: Combination of 1D-LBP and GRA. IEEE Access, 8, 137517–137529.10.1109/ACCESS.2020.3011980 Search in Google Scholar

[11] Kaplan, K., Bayram, S., Kuncan, M., Ertunc, H.M. (2014). Feature extraction of ball bearings in time-space and estimation of fault size with method of ANN. In Proceedings of the 16th Mechatronika 2014, 295–300. Search in Google Scholar

[12] Hu, Q., Si, X.-S., Qin, A.-S., Lv, Y.-R., Zhang, Q.-H. (2020). Machinery fault diagnosis scheme using redefined dimensionless indicators and mRMR feature selection. IEEE Access, 8, 40313–40326.10.1109/ACCESS.2020.2976832 Search in Google Scholar

[13] Meng, T., Jing, X., Yan, Z., Pedrycz, W. (2020). A survey on machine learning for data fusion. Information Fusion, 57, 115–129.10.1016/j.inffus.2019.12.001 Search in Google Scholar

[14] Snoek, C.G., Worring, M., Smeulders, A.W. (2005). Early versus late fusion in semantic video analysis. In Proceedings of the 13th Annual ACM International Conference on Multimedia, 399–402.10.1145/1101149.1101236 Search in Google Scholar

[15] Liu, Y., He, B., Liu, F., Lu, S., Zhao, Y. (2016). Feature fusion using kernel joint approximate diagonalization of eigen-matrices for rolling bearing fault identification. Journal of Sound and Vibration, 385, 389–401.10.1016/j.jsv.2016.09.018 Search in Google Scholar

[16] Cai, H., Qu, Z., Li, Z., Zhang, Y., Hu, X., Hu, B. (2020). Feature-level fusion approaches based on multimodal EEG data for depression recognition. Information Fusion, 59, 127–138.10.1016/j.inffus.2020.01.008 Search in Google Scholar

[17] Tian, Y., Wang, Z., Zhang, L., Lu, C., Ma, J. (2018). A subspace learning-based feature fusion and open-set fault diagnosis approach for machinery components. Advanced Engineering Informatics, 36, 194–206.10.1016/j.aei.2018.04.006 Search in Google Scholar

[18] Roweis, S.T., Saul, L.K. (2000). Nonlinear dimensionality reduction by locally linear embedding. Science, 290 (5500), 2323–2326. Search in Google Scholar

[19] He, X., Niyogi, P. (2004). Locality preserving projections. Advances in Neural Information Processing Systems, 16 (16), 153–160. Search in Google Scholar

[20] Sugiyama, M. (2007). Dimensionality reduction of multimodal labeled data by local fisher discriminant analysis. Journal of Machine Learning Research, 8 (5), 1027–1061. Search in Google Scholar

[21] Wang, Z., Ruan, Q., An, G. (2016). Facial expression recognition using sparse local Fisher discriminant analysis. Neurocomputing, 174, 756–766.10.1016/j.neucom.2015.09.083 Search in Google Scholar

[22] Zhu, Q., Liu, Q., Qin, S.J. (2017). Concurrent quality and process monitoring with canonical correlation analysis. Journal of Process Control, 60, 95–103.10.1016/j.jprocont.2017.06.017 Search in Google Scholar

[23] Zhuang, X., Yang, Z., Cordes, D. (2020). A technical review of canonical correlation analysis for neuroscience applications. Human Brain Mapping, 41 (13), 3807–3833.10.1002/hbm.25090 Search in Google Scholar

[24] Izenman, A.J. (2013). Linear discriminant analysis. In Modern Multivariate Statistical Techniques. Springer, 237–280.10.1007/978-0-387-78189-1_8 Search in Google Scholar

[25] Wold, S., Esbensen, K., Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2 (1–3), 37–52.10.1016/0169-7439(87)80084-9 Search in Google Scholar

[26] Ke, X., Yuan, F., Cheng, E. (2020). Integrated optimization of underwater acoustic ship-radiated noise recognition based on two-dimensional feature fusion. Applied Acoustics, 159, 107057.10.1016/j.apacoust.2019.107057 Search in Google Scholar

[27] Chen, Z., Ding, S.X., Peng, T., Yang, C., Gui, W. (2017). Fault detection for non-Gaussian processes using generalized canonical correlation analysis and randomized algorithms. IEEE Transactions on Industrial Electronics, 65 (2), 1559–1567. Search in Google Scholar

[28] Lai, P.L., Fyfe, C. (2000). Kernel and nonlinear canonical correlation analysis. International Journal of Neural Systems, 10 (05), 365–377.10.1142/S012906570000034X11195936 Search in Google Scholar

[29] Yuan, Y., Lu, P., Xiao, Z., Liu, J., Wu, X. (2015). A novel supervised CCA algorithm for multiview data representation and recognition. In Chinese Conference on Biometric Recognition. Springer, 702–709. ISBN 978-3-319-25417-3.10.1007/978-3-319-25417-3_82 Search in Google Scholar

[30] Wu, Z., Mao, K., Ng, G.W. (2019). Enhanced feature fusion through irrelevant redundancy elimination in intra-class and extra-class discriminative correlation analysis. Neurocomputing, 335, 105–118.10.1016/j.neucom.2019.01.029 Search in Google Scholar

[31] Peng, H., Long, F., Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27 (8), 1226–1238.10.1109/TPAMI.2005.15916119262 Search in Google Scholar

[32] Zuobin, W., Kezhi, M., Ng, G.W. (2017). Effective feature fusion for pattern classification based on intra-class and extra-class discriminative correlation analysis. In 2017 20th International Conference on Information Fusion. IEEE, 1–8. ISBN 978-1-5090-4582-2.10.23919/ICIF.2017.8009795 Search in Google Scholar

[33] Yin, W., Osher, S., Goldfarb, D., Darbon, J. (2008). Bregman iterative algorithms for \ell_1-minimization with applications to compressed sensing. SIAM Journal on Imaging Sciences, 1 (1), 143–168.10.1137/070703983 Search in Google Scholar

[34] Brown, G., Pocock, A., Zhao, M.J., Lujan, M. (2012). Conditional likelihood maximisation: A unifying framework for information theoretic feature selection. The Journal of Machine Learning Research, 13 (1), 27–66. Search in Google Scholar

[35] Wang, Y., Cang, S., Yu, H. (2019). Mutual information inspired feature selection using kernel canonical correlation analysis. Expert Systems with Applications: X, 4, 100014. Search in Google Scholar

[36] Van der Maaten, L., Hinton, G. (2008). Visualizing data using t-SNE. Journal of Machine Learning Research, 9 (11), 2579–2605. Search in Google Scholar

[37] Noble, W.S. (2006). What is a support vector machine? Nature Biotechnology, 24 (12), 1565–1567.10.1038/nbt1206-156517160063 Search in Google Scholar

[38] Sun, T., Chen, S. (2007). Locality preserving CCA with applications to data visualization and pose estimation. Image and Vision Computing, 25 (5), 531–543.10.1016/j.imavis.2006.04.014 Search in Google Scholar

[39] Guo, C., Wu, D. (2019). Canonical correlation analysis (CCA) based multi-view learning: An overview. arXiv:1907.01693 [cs.LG]. Search in Google Scholar

[40] Breiman, L. (2001). Random forest. Machine Learning, 45, 5–32.10.1023/A:1010933404324 Search in Google Scholar

[41] Xu, S. (2018). Bayesian Naïve Bayes classifiers to text classification. Journal of Information Science, 44 (1), 48–59.10.1177/0165551516677946 Search in Google Scholar

[42] Akpudo, U.E., Hur, J.W. (2020). Intelligent solenoid pump fault detection based on MFCC features, LLE and SVM. In 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC). IEEE, 404–408. ISBN 978-1-7281-4986-8.10.1109/ICAIIC48513.2020.9065282 Search in Google Scholar

[43] Jiang, L., Tan, H., Li, X., Yang, D. (2021). A novel MPELPP-ELM recognition method for the fault diagnosis of spiral bevel gears. Shock and Vibration, 2021, 5552048. Search in Google Scholar

[44] Li, Y., Dai, W., Zhang, W. (2020). Bearing fault feature selection method based on weighted multidimensional feature fusion. IEEE Access, 8, 19008–19025.10.1109/ACCESS.2020.2967537 Search in Google Scholar

[45] Yu, X., Dong, F., Ding, E., Wu, S., Fan, C. (2017). Rolling bearing fault diagnosis using modified LFDA and EMD with sensitive feature selection. IEEE Access, 6, 3715–3730.10.1109/ACCESS.2017.2773460 Search in Google Scholar

[46] Zhao, X., Jia, M. (2018). Fault diagnosis of rolling bearing based on feature reduction with global-local margin Fisher analysis. Neurocomputing, 315, 447–464.10.1016/j.neucom.2018.07.038 Search in Google Scholar

[47] Li, H., Sun, J., Ma, H., Tian, Z., Li, Y. (2019). A novel method based upon modified composite spectrum and relative entropy for degradation feature extraction of hydraulic pump. Mechanical Systems and Signal Processing, 114, 399–412.10.1016/j.ymssp.2018.04.040 Search in Google Scholar

eISSN:
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Sprache:
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Fachgebiete der Zeitschrift:
Technik, Elektrotechnik, Mess-, Steuer- und Regelungstechnik