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International Journal of Automotive Technology
Erschienen in:

09.12.2024 | Body and Safety, Chassis, Human Factors and Ergonomics, Materials and Recycling, Noise, Vibration and Harshness, Transmission and Driveline, Vehicle Dynamics and Control

A Study on Abnormal Detection of High-Speed Bearings for Electric Vehicle Drive Systems

verfasst von: Jin Yong Kim, Doo Ho Lee, Do Hyun Jung

Erschienen in: International Journal of Automotive Technology | Ausgabe 2/2025

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Abstract

Recently, as the development of high-performance electric vehicles is actively progressing, there is a strong demand for abnormality detection technology of high-speed bearings installed in drive systems. In this study, abnormal modes were analyzed for high-speed bearings applied to motors and reducers among electric vehicle drive systems, and abnormality reproduction specimens were developed for inner ring raceway flaking failures, which show the most failure cases in the field. Data for detecting anomalies were collected by installing a reducer with an anomaly reproduction specimen inserted into a transmission dynamo, and an effective method for detecting normal and abnormal states was proposed by extracting time domain and frequency domain features from the collected data. The proposed anomaly detection method will be used in the future for high-speed bearing diagnosis and residual life prediction technology development.
Vorheriger Artikel Experimental Analysis of Cooling Performance and Fuel Consumption Using Engine Test-Cell Environment
Nächster Artikel Study of Cooling Performance of Liquid-Cooled EV Battery Module According to the TIM Compression Ratio

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Literatur
Choi, Y. C. and Kim, Y. H. (2003). Fault detection in a ball bearing system using minimum variance cepstrum. In: 32nd International Congress and Exposition on Noise Control Engineering, pp. 1492−1499.
Cordoba-Arenas, A., Zhang, J., Rizzoni, G. (2013). Diagnostics and prognostics needs and requirements for electrified vehicles powertrains. In: 7th IFAC Symposium on Advances in Automotive Control, pp. 524−529.
Doguer, T. and Strackeljan, J. (2009). Vibration analysis using time domain methods for the detection of small roller bearing defects. In: 8th International Conference on Vibrations in Rotating Machines, pp. 1−12.
Kankar, P. K., Sharma, S. C., & Harsha, S. P. (2011). Rolling element bearing fault diagnosis using wavelet transform. International Journal of Neurocomputing, 1(21), 1638–1645.MATH
Lee, D. H., Choo, H. S., & Jeong, J. G. (2023). Anomaly detection based on 1D-CNN-LSTM auto-encoder for bearing data. Transaction of Information Science and Applications, 20(1), 1–6.MATH
Na, S. G., Yang, I. B., & Heo, H. (2014). Abnormality detection via SVDD technique of motor-generator system in hev. International Journal of Automotive Technology, 15(4), 637–643.MATH
Seo, Y. H., Kim, S. R., Kim, B. K., & Ma, P. S. (2019). State index of bearing of wind turbine under variable loading conditions to predict remaining useful life. Transaction of Korea Wind Energy Association, 10(3), 22–30.MATH
Trajin, B., Regnier, J., Faucher, J. (2008). Indicator for bearing fault detection in asynchronous motors using stator current spectral analysis. In International symposium on industrial electronics, Cambridge, UK, 30 June–2 July 2008 (pp. 570−575). https://​doi.​org/​10.​1109/​ISIE.​2008.​4676911
Metadaten
Titel
A Study on Abnormal Detection of High-Speed Bearings for Electric Vehicle Drive Systems
verfasst von
Jin Yong Kim
Doo Ho Lee
Do Hyun Jung
Publikationsdatum
09.12.2024
Verlag
The Korean Society of Automotive Engineers
Erschienen in
International Journal of Automotive Technology / Ausgabe 2/2025
Print ISSN: 1229-9138
Elektronische ISSN: 1976-3832
DOI
https://doi.org/10.1007/s12239-024-00164-x

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