다중 합성곱 모델을 이용한 R-EPS 차량 내부 소음 예측

Abstract

Because dealing with noises in automobile becomes more important, it is valuable to predict in-vehicle noise levels and use them for the product noise design. With the recent development of artificial intelligence, many studies have attempted to use deep learning models for various types of data generated in automobile industry. However, to the best of our knowledge, no studies have been conducted to predict in-vehicle noise levels based on deep learning models. In this study, we propose a deep learning framework that can predict in-vehicle noise levels and identify the causes of noises. Our framework is developed to recognize in-vehicle noise levels with automobile acceleration data from various locations of electric power steering devices. Our deep learning framework consists of several convolutional neural backbone networks to extract representation vectors for each acceleration axis. In addition, acceleration data are converted into a spectrogram through the short-term Fourier transformation technique, and high frequency bands in the spectrogram are removed to better represent the input data. We demonstrated that our proposed framework is suitable for predicting in-vehicle noises and identifying the major causes of noises. We expect that the explanation for prediction results will be helpful in the design low-noise vehicles.