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Full Vehicle Global Modal Identification Based on Deep Neural Network
ISSN: 0148-7191, e-ISSN: 2688-3627
Published August 31, 2021 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Full vehicle modal identification is a major challenge for both experimental and simulated modal results. A global modal is usually masked by nearby local modes, so that even well-experienced engineers have difficulty to identify vehicle modes efficiently. Besides different vehicle configurations e.g. SUV, MPV and hatchback can make the challenge even greater, since the same modal for them will present different characteristics.
This paper proposes a deep neural network for vehicle modal identification. This method takes advantage of the deep learning method which has achieved outstanding performance in language translation, computer vision and image processing. It also has the potentiality to improve modal identification efficiency.
In general commercial neural network applications, a large number of data is available for training to achieve a robust output. However, the training data for a vehicle modal identification is always limited, as a result, training the network becomes difficult. This paper proposes an architecture of a multi-layer neural network. Each layer of the neural network has been optimized to gain a robust output with limited training data.
In the end this paper focuses on modal identification from FEA analysis. Several full vehicle FEA modes have been established, and the modals from 0~50Hz have been calculated using finite element method. The modal identification shows encouraging results in which the proposed neural network is able to identify the global mode at certain degree of confidence.
CitationLee, Z., "Full Vehicle Global Modal Identification Based on Deep Neural Network," SAE Technical Paper 2021-01-1113, 2021, https://doi.org/10.4271/2021-01-1113.
Data Sets - Support Documents
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- Coyette , J. , Lielens , G. , Van den Nieuwenhof , B. , Bertolini , C. et al. From Body in White to Trimmed Body Models in the Low Frequency Range: a New Modeling Approach SAE Technical Paper 2007-01-2340 2007 https://doi.org/10.4271/2007-01-2340
- Sczibor , V. , Alves , P. , and Bringhenti , I. Modal Parameter Estimation on Automotive Development SAE Technical Paper 2012-36-0641 2012 https://doi.org/10.4271/2012-36-0641
- Jovanović , O. Identification of Dynamic System Using Neural Network The Scientific Journal FACTA Universities. Architecture and Civil Engineering 1 4 1997
- Chu , S.R. , Shoureshi , R. , and Tenorio , M. Neural Networks for System Identification IEEE Control Systems Magazine 1989 31 34 10.1109/CDC.1989.70114