| Deep Learning-Based Vehicle DTC Prognosis Approach |
| Hong-Bae Jun1, Hansom Kim1, Saeyan Eom2, Seonghyun Jeon3, Seungbum Ha1, Sewoong Jung3, Beomkyu Park4 |
1Department of Industrial and Data Engineering, Hongik University, 94, Wausan-ro, Mapo-gu, Seoul 04066, Korea
2Department of Urban Design and Planning, Hongik University, 94, Wausan-ro, Mapo-gu, Seoul 04066, Korea
3Department of Computer Engineering, Hongik University, 94, Wausan-ro, Mapo-gu, Seoul 04066, Korea
4Commercial Vehicle System Development Team, Hyundai Motor Group, Namyang R&D, Hwaseong, Gyeonggi-do 18280, Korea |
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Received: January 21, 2025; Revised: March 31, 2025 Accepted: April 17, 2025. Published online: June 11, 2025. |
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| ABSTRACT |
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In recent years, advancements in sensor technologies, wired and wireless communications, computing, and artificial intelligence have significantly enhanced our ability to monitor, diagnose, and predict the health of vehicles. The increasing complexity, automation, and intelligence of vehicle systems have amplified the demand for predicting vehicle conditions. Recognizing this trend, numerous automotive companies have shown a keen interest in leveraging vehicle Diagnostic Trouble Code (DTC)-related sensor data for advanced diagnosis and prediction, aiming to enhance customer satisfaction and service efficiency. This study addresses the challenge of predicting DTC occurrences using collected DTC-related sensor data from commercial vehicles. To tackle this issue, we employ three Deep Learning (DL) methods for anomaly detection: LSTM-Autoencoder, 1D CNN-LSTM-Autoencoder, and Anomaly Transformer. To enhance the efficiency of DTC precursor time prediction, we propose a heuristic method incorporating sliding window detection and outlier detection within a multivariate ReConstruction Error (RCE) control procedure based on the best DL method. The performance of our proposed method is discussed using case examples. Finally, we deliberate on the limitations of our study and suggest potential directions for future research. |
| Key Words:
Diagnosis · Diagnostic trouble code · Deep learning · Anomaly detection · Precursor time · Vehicle fault diagnosis and prognosis |
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Corresponding Author.
Hong-Bae Jun , Email. 
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