Authors:
Ahmed Abdelfattah
1
and
Hesham Ibrahim
2
Affiliations:
1
Mechatronics Department, German University in Cairo, Egypt
;
2
Associate Professor, Mechatronics Department, German University in Cairo, Egypt
Keyword(s):
Suspension Health Monitoring, Machine Learning, Quarter-Car Model.
Abstract:
This paper investigates Knowledge-based condition monitoring of automotive suspension dampers by implementing a quarter car model (QCM). The sprung mass acceleration - frequency power spectral density curves, for different cases of performance degradation in suspension damping and different operational conditions, is provided in response to the random road disturbance of different road classes. Training and testing acceleration response data are generated by Mtalb/simulink and fed to different classification algorithms that are trained and tested to distinguish between the different damping degradation values, in order to assess their performance in terms of classification accuracy as well as their confusion matrix. In addition, the worthiness of applying Principal Component Analysis (PCA), as a dimensional reduction technique, to increase all candidate classification algorithms is explored. Finally, the results of Quadratic Support Vector Machine showed the best performance in terms
of accuracy and confusion matrix, while using dimensional reduction turned to be inefficient.
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