Parser for diagnostic trouble codes (DTCs) used by vehicle on-board diagnostics (OBD). Resolves all digits of the code and outputs the resulting error information (e.g. vehicle part and descriptions). The parser currently supports 5090 DTCs from all categories, i.e., powertrain, body, chassis and user network, both generic and manufacturer-specific ones. If you miss DTCs, please don't hesitate to file an issue or directly add them in a PR.
$ git clone https://github.com/tbohne/dtc_parser.git
$ cd dtc_parser/
$ pip install .
$ python dtc_parser/parser.py --code CODE
$ python dtc_parser/parser.py --code P0112
... parsing P0112 ...
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VEHICLE PART: powertrain (engine, transmission, and associated accessories)
CODE TYPE: standardized (SAE) code, aka generic code
VEHICLE SUBSYSTEM: fuel and air metering
FAULT DESCRIPTION: intake air temperature sensor 1 circuit low
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<VEHICLE_PART>_<CODE_TYPE>_<VEHICLE_SUBSYSTEM>_<FAULT_DESCRIPTION>
@inproceedings{10.1145/3587259.3627546,
author = {Bohne, Tim and Windler, Anne-Kathrin Patricia and Atzmueller, Martin},
title = {A Neuro-Symbolic Approach for Anomaly Detection and Complex Fault Diagnosis Exemplified in the Automotive Domain},
year = {2023},
isbn = {9798400701412},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3587259.3627546},
doi = {10.1145/3587259.3627546},
booktitle = {Proceedings of the 12th Knowledge Capture Conference 2023},
pages = {35–43},
numpages = {9},
location = {Pensacola, FL, USA},
series = {K-CAP '23}
}