Abstract
Traditional accelerometers suffer the issue of low signal to noise ratio (SNR) and the measured signals generally show more completed modulation due to the undesired installation. On-Rotor Sensing (ORS) technology has been proposed to solve this problem and achieve the low-cost and effective condition monitoring of rotating machines. This paper aims to develop more accurate and robust diagnosis of motor bearing faults based on the vibration of the ORS. Based on the general layouts of a motor driving system, the structural design and device integration of the ORS system are performed to make it be installed on the shaft end easier and achieve non-invasive measurement. Then the ORS outputs for the diagnosis of different types of bearing faults are deduced theoretically. Experiments of three different motors are carried out to validate the performance of the proposed ORS method. Compared to the On House Sensing (OHS) by the traditional accelerometer, the proposed ORS technology can provide more robust incipient motor bearing fault detection (e.g., inner race and outer race faults), with higher fault frequency amplitudes in the spectrum of low frequency bands.
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References
Gangsar, P., Tiwari, R.: Signal based condition monitoring techniques for fault detection and diagnosis of induction motors: a state-of-the-art review. Mech. Syst. Signal Process. 144(4), 106908 (2020)
Wang, Z., Yang, J., Li, H., Zhen, D., Gu, F., Ball, A.: Improved cyclostationary analysis method based on TKEO and its application on the faults diagnosis of induction motors. ISA Trans. 128, 513–530 (2021)
Kliman, G.B., Stein, J.: Induction motor fault detection via passive current monitoring. In: Proceedings of the International Conference on Electrical Machines, Cambridge, MA, pp. 13–17, August 1990
Nirwan, N.W., Ramani, H.B.: Condition monitoring and fault detection in roller bearing used in rolling mill by acoustic emission and vibration analysis. Mater. Today Proc. 51, 344–354 (2022)
Al-Musawi, A.K., Anayi, F., Packianather, M.: Three-phase induction motor fault detection based on thermal image segmentation. Infrared Phys. Technol. 104, 103140 (2019)
Nandi, S., Toliyat, H.A., Li, X.: Condition monitoring and fault diagnosis of electrical motors—a review. IEEE Trans. Energy Convers. 20(4), 719–729 (2005)
Jiménez, S., Keogh, P.S.: A self-sensing and self-actuating active rotor with an algorithmic direct search controller. IEEE/ASME Trans. Mechatron. 22, 1 (2017). https://doi.org/10.1109/TMECH.2017.2668762
Yao, R., Jiang, H., Wu, Z., et al.: Periodicity-enhanced sparse representation for rolling bearing incipient fault detection. ISA Trans. 118(1), 219–237 (2021)
Jiménez, S., Cole, M., Keogh, P.S.: Vibration sensing in smart machine rotors using internal MEMS accelerometers. J. Sound Vib. 377, 58–75 (2016)
Yongfang, Z., Xia, W., Zhiguo, X., et al.: Research status of vibration sensors for mechanical equipment health monitoring. Mater. Rev. 34(13), 10 (2020)
Pedotti, L., Zago, R.M., Giesbrecht, M., et al.: Low-cost MEMS accelerometer network for rotating machine vibration diagnostics. IEEE Instrum. Meas. Mag. 23(7), 25–33 (2020)
Elnady, M.E., Abdelbary, A., Sinha, J.K., et al.: FE and experimental modeling of on-shaft vibration measurement. In: Proceedings of the 15th International Conference on Aerospace Sciences and Aviation Technology (2013)
Xu, Y., et al.: Orthogonal on-rotor sensing vibrations for condition monitoring of rotating machines. J. Dyn. Monit. Diagn. 1(1), 29–36 (2022)
Feng, G., Hu, N., Mones, Z., et al.: An investigation of the orthogonal outputs from an on-rotor MEMS accelerometer for reciprocating compressor condition monitoring. Mech. Syst. Signal Process. 76–77, 228–241 (2016)
Acknowledgements
This work has been supported by Guangdong Science and Technology Department (No. 2020KTSCX188), Beijing Municipal Science and Technology Commission (No. Z201100008320004).
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Shi, D. et al. (2023). Diagnosis of Motor Bearing Faults Using the Vibration of an On-Rotor Sensing Method. In: Zhang, H., Ji, Y., Liu, T., Sun, X., Ball, A.D. (eds) Proceedings of TEPEN 2022. TEPEN 2022. Mechanisms and Machine Science, vol 129. Springer, Cham. https://doi.org/10.1007/978-3-031-26193-0_96
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DOI: https://doi.org/10.1007/978-3-031-26193-0_96
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