[1] Bloch, H.P. and Geitner, F.K., “Machinery component maintenance and repair,” Elsevier, Gulf Professional Pub, 2005.
[2] Randall, R.B., “Vibration-based condition monitoring: industrial, automotive and aerospace applications,” John Wiley & Sons, 2021.
[3] McInerny, S.A. and Dai, Y., “Basic vibration signal processing for bearing fault detection,” IEEE Transactions on education, 46(1), pp.149-156, 2003 , doi: 10.1109/TE.2002.808234.
[4] Dolenc, B., Boškoski, P. and Juričić, Đ., “Distributed bearing fault diagnosis based on vibration analysis,” Mechanical Systems and Signal Processing, 66, pp.521-532, 2016, doi: 10.1016/j.ymssp.2015.06.007.
[5] Zarei, J., Tajeddini, M.A. and Karimi, H.R., “Vibration analysis for bearing fault detection and classification using an intelligent filter,” Mechatronics, 24(2), pp.151-157, 2014, doi: 10.1016/j.mechatronics.2014.01.003.
[6] Khadersab, A. and Shivakumar, S., “Vibration analysis techniques for rotating machinery and its effect on bearing faults,” Procedia Manufacturing, 20, pp.247-252, 2018, doi: 10.1016/j.promfg.2018.02.036.
[7] Patidar, S. and Soni, P.K., “An overview on vibration analysis techniques for the diagnosis of rolling element bearing faults,” International Journal of Engineering Trends and Technology (IJETT), 4(5), pp.1804-1809, 2013.
[8] Souad, S.L., Azzedine, B. and Meradi, S., “Fault diagnosis of rolling element bearings using artificial neural network,” International Journal of Electrical and Computer Engineering, 10(5), p.5288, 2020, doi: 10.11591/ijece.v10i5.pp5288-5295.
[9] Kumbhar, S.G., Desavale, R.G. and Dharwadkar, N.V., “Fault size diagnosis of rolling element bearing using artificial neural network and dimension theory,” Neural Computing and Applications, 33(23), pp.16079-16093, 2021, doi:
10.1007/s00521-021-06228-8.
[10] Chao, K.C., Chou, C.B. and Lee, C.H., “Online domain adaptation for rolling bearings fault diagnosis with imbalanced cross-domain data,” Sensors, 22(12), p.4540, 2022, doi:
10.3390/s22124540.
[11] Rajabi, S., Azari, M.S., Santini, S. and Flammini, F., “Fault diagnosis in industrial rotating equipment based on permutation entropy, signal processing and multi-output neuro-fuzzy classifier,” Expert systems with applications, 206, p.117754, 2022, doi:
10.1016/j.eswa.2022.117754.
[12] El Idrissi, A., Derouich, A., Mahfoud, S., El Ouanjli, N., Chojaa, H. and Chantoufi, A., “Bearing faults diagnosis by current envelope analysis under direct torque control based on neural networks and fuzzy logic—a comparative study,” Electronics, 13(16), p.3195, 2024, doi:
10.3390/electronics13163195.
[13] Kumbhar, S.G., “An integrated approach of Adaptive Neuro-Fuzzy Inference System and dimension theory for diagnosis of rolling element bearing. Measurement, 166, p.108266, 2020, doi: 10.1016/j.measurement.2020.108266.
[14] Lin, C.J. and Jhang, J.Y., “Bearing fault diagnosis using a grad-CAM-based convolutional neuro-fuzzy network,” Mathematics, 9(13), p.1502, 2021, doi: 10.3390/math9131502.
[15] Ren, Z. and Guo, J., “On fault diagnosis using image-based deep learning networks based on vibration signals,” Multimedia Tools and Applications, 83(15), pp.44555-44580, 2024, doi: 10.1007/s11042-023-17384-5.
[16] Loparo, K. A. (2024). Bearing vibration data set, Case Western Reserve University. http://www. eecs. cwru. edu/laboratory/bearing/download. Htm
[17] Rose, T. Pravin, and G. Glan Devadhas. “Detection of pH neutralization technique in multiple tanks using ANFIS controller.” Microprocessors and Microsystems 72 (2020): 102845, doi: 10.1016/j.micpro.2019.07.004.
[18] Veerakumar, S., P. Selvabharathi, and S. Sathishkumar. "RETRACTED: Power Quality Issues Compensation using ANN Techniques." In IOP Conference Series: Materials Science and Engineering, vol. 1084, no. 1, p. 012104. IOP Publishing, 2021, doi: 10.1088/1757-899X/1084/1/012104.
