Abstract
The bearing element is an essential part in different mechanical equipment and rotating machinery. An unexpected failure of the bearing may cause substantial economic losses. Vibration analysis has been used as one of the most preferred measured signal to detect the bearing related fault. In this paper, an online method for automatic rolling-element bearing fault diagnosis using Multiclass Support Vector Machine MSVM is proposed. Firstly, the Empirical Mode Decomposition EMD technique is used to decompose the vibration signal into a finite number of stationary intrinsic mode functions (IMFs), then different parameters are calculated based FFT algorithm. The frequency analysis results of different EMD intrinsic mode functions for different vibration signals show that these intrinsic mode functions will change in different frequency bands when a bearing fault occurs. Therefore, to identify roller bearing fault class, the Locality Sensitivity Discriminant Analysis LSDA algorithm is used to transform the selected feature to low-dimensional space. The final features could serve as input vectors of trained MSVM. The analysis results from roller bearing signals with inner-race, out-race and ball faults show that the proposed diagnostic approach based on the MSVM as a classifier and the EMD as a signal processing technique to extract different stationary intrinsic mode functions as features can identify roller bearing fault type and severity patterns accurately and effectively even the analyzed vibration signal is acquired in the case of varying load and speed.
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Attoui, I., Fergani, N., Boutasseta, N., Oudjani, B., Bouakkaz, M.S., Bouraiou, A. (2021). Multiclass Support Vector Machine Based Bearing Fault Detection Using Vibration Signal Analysis. In: Bououden, S., Chadli, M., Ziani, S., Zelinka, I. (eds) Proceedings of the 4th International Conference on Electrical Engineering and Control Applications. ICEECA 2019. Lecture Notes in Electrical Engineering, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-15-6403-1_61
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DOI: https://doi.org/10.1007/978-981-15-6403-1_61
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