Fault Prognosis of Hydraulic Pump Based on Bispectrum Entropy and Deep Belief Network

[1] Ahmad, R., Kamaruddin, S. (2012). An overview of time-based and condition-based maintenance in industrial application. Computers & Industrial Engineering, 63 (1), 135-149.10.1016/j.cie.2012.02.002Search in Google Scholar

[2] Sun, J., Li, H., Wang, W., Ye, P. (2015). Morphological undecimated wavelet decomposition fusion algorithm and its application on fault feature extraction of hydraulic pump. Transactions of Nanjing University of Aeronautics and Astronautics, 32 (3), 268-278.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 pump. Measurement, 94, 794-805.10.1016/j.measurement.2016.09.024Search in Google Scholar

[4] Hinton, G.E., Salakhutdinov, R.R. (2006). Reducing the dimensionality of data with neural networks. Science, 313 (5786), 504-507.10.1126/science.1127647Search in Google Scholar

[5] Wang, Y.G., Cao, F.L., Yuan, Y.B. (2011) A study on effectiveness of extreme learning machine. Neurocomputing, 74 (16), 2483-2490.10.1016/j.neucom.2010.11.030Search in Google Scholar

[6] Kuremoto, T., Kimura, S., Kobayashi, K., Obayashi, M. (2014). Time series forecasting using a deep belief network with restricted Boltzmann machines. Neurocomputing, 137, 47-56.10.1016/j.neucom.2013.03.047Search in Google Scholar

[7] Theodorakopoulos, I., Kastaniotis, D., Economou, G., Fotopoulos, S. (2014). Pose-based human action recognition via sparse representation in dissimilarity space. Journal of Visual Communication and Image Representation, 25 (1), 12-23.10.1016/j.jvcir.2013.03.008Search in Google Scholar

[8] Lefebvre, G., Berlemont, S., Mamalet, F., Garcia, C. (2013). BLSTM-RNN based 3D gesture classification. In Artificial Neural Networks and Machine Learning – ICANN 2013. Springer, LNCS 8131, 381-388.10.1007/978-3-642-40728-4_48Search in Google Scholar

[9] Tran, V.T., Althobiani, F., Ball, A. (2014). An approach to fault diagnosis of reciprocation compressor valves using Teager-Kaiser energy operator and deep belief networks. Expert Systems with Applications, 41 (9), 4113-4122.10.1016/j.eswa.2013.12.026Search in Google Scholar

[10] Roy, P.P., Chherawala, Y., Cheriet, M. (2014). Deep belief network based rescoring approach for handwritten word recognition. In 14th International Conference on Frontiers in Handwriting Recognition. IEEE, 506-511.10.1109/ICFHR.2014.91Search in Google Scholar

[11] Mohamed, A.R., Dahl, G.E., Hinton, G. (2012). Acoustic modeling using deep belief networks. IEEE Transactions on Audio, Speech, & Language Processing, 20 (1), 14-22.10.1109/TASL.2011.2109382Search in Google Scholar

[12] Lee, H., Grosse, R., Ranganath, R., Ng, A.Y. (2011). Unsupervised learning of hierarchical representations with convolutional deep belief networks. Communications of the ACM, 54 (10), 95-103.10.1145/2001269.2001295Search in Google Scholar

[13] Shen, G.J., Stephen, M., Xu, Y.C., White, P. (2014). Theoretical and experimental analysis of bispectrum of vibration signals for fault diagnosis of gears. Mechanical Systems and Signal Processing, 43 (1-2), 76-89.10.1016/j.ymssp.2013.08.023Search in Google Scholar

[14] Gu, F., Shao, Y., Hu, N., Naid, A., Ball, A.D. (2011). Electrical motor current signal analysis using a modified bispectrum for fault diagnosis of downstream mechanical equipment. Mechanical Systems and Signal Processing, 25 (1), 360-372.10.1016/j.ymssp.2010.07.004Search in Google Scholar

[15] Zhou, Y.B., Liu, Y.B., Li, H., Teng, W., Li, Z. (2013). Fault feature extraction for gear crack based on bispectral entropy. China Mechanical Engineering, 24 (2), 190-194.Search in Google Scholar

[16] Hou, S., Li, Y., Xu, Y., Ma, C. (2016). Applications of dual-tree complex wavelet transform and bispectrum in roller bearing fault diagnosis. Noise and Vibration Control, 36 (5), 133-136.Search in Google Scholar

[17] Jin, Z.Y., Lu, Y.G., Zhang, B., Yao, X.L. (2016). Applications of bispectrum analysis of intrinsic model function in the blade rack recognition. Noise and Vibration Control, 36 (5), 153-156.Search in Google Scholar

[18] Lee, M.G., Kim, C., Pavlina, E.J., Barlat, F. (2011). Advances in sheet forming materials modeling, numerical simulation, and press technologies. Journal of Manufacturing Science and Engineering, 133 (6), 1001-1012.10.1115/1.4005117Search in Google Scholar

[19] Huang, J.Y., Pan, H.X., Bi, S.H., Cui, B.Z. (2012). Fault pattern recognition based on bispectrum entropy model. Acta Armamentarii, 33 (6), 718-722.Search in Google Scholar

[20] Qiao, J.F., Pan, G.Y., Han, H.G. (2015). Design and application of continuous deep belief network. Acta Automatica Sinica, 41 (12), 2138-2146.Search in Google Scholar

[21] Chen, C., Liu, K., Kehtarnavaz, N. (2016). Real-time human action recognition based on depth motion maps. Journal of Real Time Image Processing, 12 (1), 155-163.10.1007/s11554-013-0370-1Search in Google Scholar

[22] Lu, X.H., Pan, D.F., Han, K., Li, J.S. (2016). Railway short-term wind speed prediction based on improved QPSO-WNN rolling algorithm. Journal of Railway Science and Engineering, 13 (5), 978-984.Search in Google Scholar

[23] Sun, J., Li, H.R., Tian, Z.K. (2016). A degradation feature extraction method for hydraulic pumps based on fusion of sensitive components. Chinese Journal of Scientific Instrument, 37 (6), 1290-1298.Search in Google Scholar

[24] Huang, B., Feng, G., Tang, X., Gu, J.X., Xu, G., Cattley, R., Gu, F., Ball, A.D. (2019). A performance evaluation of two bispectrum analysis methods applied to electrical current signals for monitoring induction motor-driven systems. Energies, 12 (8), 1438.10.3390/en12081438Search in Google Scholar

[25] Zhou, Y.B., Liu, Y.B., Li, H., Teng, W., Li, Z. (2013). Fault feature extraction for gear crack based on bispecral entropy. China Mechanical Engineering, 24 (2), 190-194.Search in Google Scholar

[26] Tamilselvan, P., Wang, P. (2013). Failure diagnosis using deep belief learning based health state classification. Reliability Engineering & System Safety, 115, 124-135.10.1016/j.ress.2013.02.022Search in Google Scholar

[27] Fu, Y., Zhang, Y., Qiao, H., Li, D., Zhou, H., Leopold, J. (2015). Analysis of feature extracting ability for cutting state monitoring using deep belief networks. Procedia CIRP, 31, 29-34.10.1016/j.procir.2015.03.016Search in Google Scholar

[28] Shao, H., Jiang, H., Zhang, X., Niu, M. (2015). Rolling bearing fault diagnosis using an optimization deep belief network. Measurement Science & Technology, 26 (11), 1-17.10.1088/0957-0233/26/11/115002Search in Google Scholar

[29] Zhao, G.Q., Liu, Y.F., Zhang, B., Hu, C. (2017). Lithium-ion battery remaining useful life prediction with Deep Belief Network and Relevance Vector Machine. In IEEE International Conference on Prognostics and Health Management (ICPHM). IEEE, 7-13.10.1109/ICPHM.2017.7998298Search in Google Scholar

[30] Zhao, R., Yan, R.Q., Chen, Z.H., Mao, K.Z., Wang, P., Gao, R.X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213-237.10.1016/j.ymssp.2018.05.050Search in Google Scholar