Neuro-Symbolic Vehicle Diagnosis State Machine

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.

Neuro-Symbolic Architecture

Essentially, the Diag. Circuit is implemented through this repository, the Neural Network Architectures through oscillogram_classification, and the Knowledge Graph through obd_ontology.

Dependencies

• for Python requirements, cf. requirements.txt
• Apache Jena Fuseki: SPARQL server hosting / maintaining the knowledge graph
• optional: CustomerXPS: expert system that deals with customer complaints

Installation (from source)

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").

Installation (Docker)

$ git clone https://github.com/tbohne/vehicle_diag_smach.git
$ cd vehicle_diag_smach/
$ docker build -t diag_system -f docker/Dockerfile .

Optional Installation of Customer XPS

Optionally, to use the customer XPS, create a .jar file with the py4j and d3web dependencies (cf. CustomerXPS).

Source Usage

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

Docker Usage

Initial run (only for initial setup)

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").

Run existing container

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

State Machine Architecture

Hierarchical Diagnosis State Machine (High-Level)

Embedded Diagnosis State Machine (Low-Level)

Fault Isolation Result Example

Related Publications

@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}
}