Iterative, hybrid neuro-symbolic approach for anomaly detection and complex fault diagnosis, enabling knowledge-based (symbolic) methods to complement (neural) machine learning methods and vice versa. Explainability is indispensable for diagnosis and arises naturally in the system through the specific interplay of neural and symbolic methods. The reasoning of the system is encoded in a state machine architecture.
Essentially, the Diag. Circuit is implemented through this repository, the Neural Network Architectures through oscillogram_classification, and the Knowledge Graph through obd_ontology.
requirements.txt
Install diagnosis state machine and dependencies:
$ git clone https://github.com/tbohne/vehicle_diag_smach.git
$ cd vehicle_diag_smach/
$ pip install .
Set up Apache Jena Fuseki server:
$ cd ..
$ curl -L https://dlcdn.apache.org/jena/binaries/apache-jena-fuseki-VERSION.tar.gz > apache-jena-fuseki-VERSION.tar.gz
$ tar -xvzf apache-jena-fuseki-VERSION.tar.gz
$ chmod +x apache-jena-fuseki-VERSION/fuseki-server
For configuration, i.e., hosting the knowledge graph, cf. obd_ontology (section "Launch knowledge graph from RDF serialization (e.g. .nt
/ .owl
/ .ttl
file").
$ git clone https://github.com/tbohne/vehicle_diag_smach.git
$ cd vehicle_diag_smach/
$ docker build -t diag_system -f docker/Dockerfile .
Optionally, to use the customer XPS, create a .jar
file with the py4j and d3web dependencies (cf. CustomerXPS).
Run server (knowledge graph) from Apache Jena Fuseki root directory (runs at localhost:3030
):
$ ./apache-jena-fuseki-VERSION/fuseki-server
Run state machine (in vehicle_diag_smach/
):
$ python vehicle_diag_smach/high_level_smach.py
Optionally run customer XPS server (in CustomerXPS/
):
$ java -jar out/artifacts/CustomerXPS_jar/CustomerXPS.jar
Allow local docker container to display visualizations on host system (connection to XServer):
$ xhost +local:docker
Create and start docker container with visualizations on host system (Apache Jena Fuseki server runs at CONTAINER_IP:3030
):
$ docker run -v /tmp/.X11-unix/:/tmp/.X11-unix -e DISPLAY=unix$DISPLAY -ti diag_system
For configuration, i.e., hosting the knowledge graph, cf. obd_ontology (section "Launch knowledge graph from RDF serialization (e.g. .nt
/ .owl
/ .ttl
file").
After restarts, repeat the above XServer settings if necessary. Start already existing container (with persistent knowledge graph hosted on CONTAINER_IP:3030
):
$ docker start -i CONTAINER_ID
Optionally run customer XPS server (in CustomerXPS/
):
$ java -jar out/artifacts/CustomerXPS_jar/CustomerXPS.jar
Hierarchical Diagnosis State Machine (High-Level)
Embedded Diagnosis State Machine (Low-Level)
@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}
}