Research on fault feature extraction and early warning of rolling bearing vibration signal of generator set

[1] Sagnak, M., Kazancoglu, Y., Ozkan, Ozen, Y. D., et al. (2020). Decision-making for risk evaluation: integration of prospect theory with failure modes and effects analysis (FMEA). International Journal of Quality & Reliability Management, 37(6/7), 939-956. Search in Google Scholar

[2] Diniz, P., Costa, L. D., Silveira, J. D., et al. (2021). Performance evaluation of controllers applied to power generator set operating with wastewater biogas. Electrical engineering, (1), 103. Search in Google Scholar

[3] Sharma, A., Upadhyay, N., Kankar, P. K., et al. (2018). Nonlinear dynamic investigations on rolling element bearings: A review. Advances in Mechanical Engineering, 10(3), 1687814018764148. Search in Google Scholar

[4] Manieri, F., Stadler, K., Morales-Espejel, G. E., et al. (2019). The origins of white etching cracks and their significance to rolling bearing failures. International Journal of Fatigue, 120, 107-133. Search in Google Scholar

[5] Cerrada, M., Sánchez, R. V., Li, C., et al. (2018). A review on data-driven fault severity assessment in rolling bearings. Mechanical Systems and Signal Processing, 99, 169-196. Search in Google Scholar

[6] Wang, H., Ni, G., Chen, J., et al. (2020). Research on rolling bearing state health monitoring and life prediction based on PCA and Internet of things with multi-sensor. Measurement, 157, 107657. Search in Google Scholar

[7] Alexandre, F. A., Lopes, W. N., Ferreira, F. I., et al. (2017). Chatter Vibration Monitoring in the Surface Grinding Process through Digital Signal Processing of Acceleration Signal. Search in Google Scholar

[8] Thomazella, R., Lopes, W. N., Aguiar, P. R., et al. (2019). Digital signal processing for self-vibration monitoring in grinding: A new approach based on the time-frequency analysis of vibration signals. Measurement, 145, 71-83. Search in Google Scholar

[9] Amarnath, M., Praveen, Krishna, I. R., (2019). Experimental investigations to assess surface contact fatigue faults in the rolling contact bearings by enhancement of sound and vibration signals. Journal of Nondestructive Evaluation, 38, 1-23. Search in Google Scholar

[10] Goyal, D., Choudhary, A., Pabla, B. S., et al.(2020). Support vector machines based non-contact fault diagnosis system for bearings. Journal of Intelligent Manufacturing, 31, 1275-1289. Search in Google Scholar

[11] Qin, Yi. (2017). A New Family of Model-Based Impulsive Wavelets and Their Sparse Representation for Rolling Bearing Fault Diagnosis. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS,1-1. Search in Google Scholar

[12] Li, H., Liu, T., Wu, X., et al. (2020). Enhanced Frequency Band Entropy Method for Fault Feature Extraction of Rolling Element Bearings. IEEE Transactions on Industrial Informatics, 16(9),5780-5791. Search in Google Scholar

[13] Li, H., Li, F., Jia, R., et al. (2021). Research on the Fault Feature Extraction of Rolling Bearings Based on SGMD-CS and the AdaBoost Framework. Energies, 14(6), 1555. Search in Google Scholar

[14] Li, J., Meng, Z., Yin, N., et al. (2021). Multi-source feature extraction of rolling bearing compression measurement signal based on independent component analysis. Measurement, 172, 108908. Search in Google Scholar

[15] Wang, T., Chu, F., (2019). Bearing fault diagnosis under time-varying rotational speed via the fault characteristic order (FCO) index based demodulation and the stepwise resampling in the fault phase angle (FPA) domain. ISA transactions, 94, 391-400. Search in Google Scholar

[16] Zhang, X., Luo, H., Li, K., et al. (2021). Time-Domain Frequency Estimation with Application to Fault Diagnosis of the UAVs Blade Damage. IEEE Transactions on Industrial Electronics, PP(99), 1-1. Search in Google Scholar

[17] Marcella, C., Lorenzo, B., Giuseppe, S., et al. (2016). Predicting Human Eye Fixations via an LSTM-based Saliency Attentive Model. IEEE Transactions on Image Processing, 27, 5142-5154. Search in Google Scholar