차량 DTC 데이터 기반 고장 상태 예지 방안에 대한 사례 연구A Case Study on Predicting the Vehicle Failure Code with Gathered Diagnostic Trouble Code Data
- Other Titles
- A Case Study on Predicting the Vehicle Failure Code with Gathered Diagnostic Trouble Code Data
- Authors
- 장명훈; 박한설; 김지인; 오정림; 전홍배
- Issue Date
- 2020
- Publisher
- 한국CDE학회
- Keywords
- Condition-Based Maintenance (CBM); Connected Car; Prognostics; DTC (Diagnostic Trouble Code)
- Citation
- 한국CDE학회 논문집, v.25, no.4, pp.358 - 365
- Journal Title
- 한국CDE학회 논문집
- Volume
- 25
- Number
- 4
- Start Page
- 358
- End Page
- 365
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/11859
- DOI
- 10.7315/CDE.2020.358
- ISSN
- 2508-4003
- Abstract
- Sudden vehicle problems while driving cause great damage to the driver. In this context, it is necessary to monitor important vehicle parts’ condition and take appropriate actions in advance based on condition analysis. This paper implements a model for predicting the occurrence of a certain failure code before 24 hours based on gathered DTC (Diagnostic Trouble Code) data with LSTM (Long Short-Term Memory)-Autoencoder. LSTM is a type of RNN (Recurrent Neural Network) that can solve data long-term dependency problems and is suitable for learning many time-series data to create classification and regression models. In particular, the model is a stacked autoencoder structure consisting of several LSTMs, showing higher accuracy than normal LSTM. The case study shows that the proposed method gives a reasonable performance on predicting the failure code.
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Collections - College of Engineering > Industrial Engineering Major > 1. Journal Articles
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