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DTCEncoder: A Swiss Army Knife Architecture for DTC Exploration, Prediction, Search and Model Interpretation

Hafeez, A. B., Alonso, E. ORCID: 0000-0002-3306-695X & Riaz, A. (2023). DTCEncoder: A Swiss Army Knife Architecture for DTC Exploration, Prediction, Search and Model Interpretation. Paper presented at the 21st IEEE International Conference on Machine Learning and Applications (ICMLA), 12-14 Dec 2022, Nassau, Bahamas. doi: 10.1109/ICMLA55696.2022.00085


Diagnostic Trouble Code (DTC) events, produced in vehicles, assist in knowing the occurrence of faults in different modules and can be used for predictive maintenance by detecting patterns. While performing Exploratory Data Analysis (EDA) or correlating specific DTC events is an easy task, searching for patterns in long multivariate DTC sequences can be very challenging. Instead of performing analysis for individual DTCs, a self-supervised approach using a Long Short-Term Memory (LSTM) network was introduced recently to perform the next DTC prediction. Despite its merits, such an approach is not interpretable for engineers who need to understand the decision- making process of the model. In this paper, we introduce the DTCEncoder, a recurrent neural network that incorporates Gated Recurrent Units (GRU) and an attention mechanism to encode DTC sequences into low dimensional representations, and that serves as a unified approach to (i) efficiently represent multivariate event sequences and predict the next event, (ii) interpret what the model learns and what it uses for the next prediction, and (iii) perform efficient semantic search for individual DTCs and DTC sequences.

Publication Type: Conference or Workshop Item (Paper)
Additional Information: © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher Keywords: Recurrent neural networks, Data analysis, Codes, Semantic search, Machine learning, Predictive models, Logic gates
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: School of Science & Technology > Computer Science
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