Digital Analytics, Causal Knowledge Acquisition and Reasoning
A Knowledge Management and Discovery Tool for Equipment Reliability Data
To improve the performance and reliability of high dependable technological systems such as nuclear power plants, advanced monitoring and health management systems are employed to inform system engineers on observed degradation processes and anomalous behaviors of assets and components. This information is captured in the form of large amount of data which can be heterogenous in nature (e.g., numeric, textual). Such a large amount of available data poses challenges when system engineers are required to parse and analyze them to track the historic reliability performance of assets and components. DACKAR tackles this challenge by providing means to organize equipment reliability data in the form of a knowledge graph. DACKAR distinguish itself from current knowledge graph-based methods in that model-based system engineering (MBSE) models are used to capture system architecture and health and performance data. MBSE models are used as skeleton of a knowledge graph; numeric and textual data elements, once processed, are associated to MBSE model elements. Such a feature opens the door to new data analytics methods designed to identify causal relations between observed phenomena.
DACKAR is structured by a set of workflows where each workflow is designed to process raw data elements (i.e., anomalies, events reported in textual form, MBSE models) and construct or update a knowledge graph. For each workflow, the user can specify the sequence of pipelines that are designed to perform specific processing actions on the raw data or the processed data within the same workflow. Specific guidelines on the formats of the raw data are provided. In addition, within the same workflow, a specific data-object is defined; in this respect, each pipeline is tasked to either process portion of the defined data-object or create knowledge graph data. The available workflows are:
- mbse_workflow: Workflow to process system and equipment MBSE models
- anomaly_workflow: Workflow to process numeric data and anomalies
- tlp_workflow: Workflow to process textual data
- kg_workflow: Workflow to construct and update knowledge graphs
- Install dependency libraries
conda create -n dackar_libs python=3.11
conda activate dackar_libs
pip install spacy==3.5 stumpy textacy matplotlib nltk coreferee beautifulsoup4 networkx pysbd tomli numerizer autocorrect pywsd openpyxl quantulum3[classifier] numpy==1.26 scikit-learn pyspellchecker contextualSpellCheck pandas- Install additional libraries
Library neo4j is a Python module that is used to communicate with Neo4j database management system,
and jupyterlab is used to execute notebook examples under ./examples/ folder.
pip install neo4j jupyterlab- Download language model from spacy (can not use INL network)
python -m spacy download en_core_web_lg
python -m coreferee install en- Install required nltk data for similarity analysis
python -m nltk.downloader all- Run the following command in your command line to install pytest:
pip install -U pytest- The tests can be run with:
cd tests
pytest pip install sphinx sphinx_rtd_theme nbsphinx sphinx-copybutton sphinx-autoapi
conda install pandoc cd docs
make html
cd _build/html
python3 -m http.serveropen your browser to: http://localhost:8000
Sphinx uses latex to export the documentation as a PDF file. Thus one needs the basic latex dependencies used to write a pdf on the system.
cd docs
make latexpdf
cd _build/latex/The PDF version of DACKAR is located at _build/latex/dackar.pdf
