소음 데이터를 이용한 딥러닝 기반의 차량 진단 기술 개발Development of Deep Learning-Based Vehicle Diagnosis Technology Using Noise Data
- Other Titles
- Development of Deep Learning-Based Vehicle Diagnosis Technology Using Noise Data
- Authors
- 노경진; 이동철; 진재민; 정인수; 장준혁
- Issue Date
- Jun-2023
- Publisher
- 한국소음진동공학회
- Keywords
- Deep Learning; Vehicle Noise; Convolutional Neural Network; Attention; Classification; Regression; 딥러닝; 차량 소음; 합성곱 신경망; 어텐션; 분류; 회귀
- Citation
- 한국소음진동공학회논문집, v.33, no.3, pp.306 - 312
- Indexed
- KCI
- Journal Title
- 한국소음진동공학회논문집
- Volume
- 33
- Number
- 3
- Start Page
- 306
- End Page
- 312
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/191914
- DOI
- 10.5050/KSNVE.2023.33.3.306
- ISSN
- 1598-2785
- Abstract
- In this paper, we propose deep learning models for fault diagnosis and noise level estimation using vehicle noise data. First, we use two spectrograms as a feature vector by converting the input signal and the signal of the separated percussive component in the input signal. For fault diagnosis, we design a classification model. Two spectrograms are respectively fed into a series of convolutional layers that includes a convolutional block attention module(CBAM) block and max-pooling.
Then, the two outputs are combined and passed through fully connected layers that is finally converted to a probability. Next, we design a regression model for noise level index estimation. We first define the noise level index using signal processing techniques and use it as a target for the deep learning model. Unlike the fault diagnosis model, the two spectrograms are combined and fed into a series of convolutional layers. Then, the output is passed through fully connected layers, and the estimated real value is rounded to the nearest integer value from 1 to 5. Experimental results showed excellent performance with an accuracy of 96 % for fault diagnosis and 86 % for noise level index estimation.
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