PSUT Portal Project Title: Knowledge Graph-Based Predictive Maintenance Assistant
Responsible Researcher: Dr. Omar Alqawasmeh
Project Description:
This project aims to develop an intelligent predictive maintenance assistant based on Knowledge Graphs, Artificial Intelligence, Machine Learning, and Natural Language Processing. The proposed framework will integrate heterogeneous maintenance data, including sensor readings, equipment and maintenance logs, failure reports, technical manuals, inspection notes, and operational data.
A Knowledge Graph will be developed to represent and connect assets, components, sensors, faults, symptoms, causes, maintenance actions, and historical failure cases. This knowledge layer will be integrated with predictive AI models to enable the system not only to predict potential failures, but also to provide explainable and evidence-based maintenance recommendations, linking predictions to possible causes, affected components, previous cases, and appropriate maintenance actions.
The proposed framework is designed to be general, scalable, and adaptable to multiple application domains, including manufacturing, energy, transportation, smart buildings, infrastructure, and other maintenance-intensive sectors.
Research Questions:
In this project, the following research questions will be investigated (these are too wide, and need to be defined/selected later on):
- How can Knowledge Graphs be used to formally represent maintenance knowledge across different domains?
- How can machine learning predictions be integrated with Knowledge Graph reasoning to provide explainable maintenance recommendations?
- How can NLP techniques extract useful maintenance knowledge from technical reports, inspection logs, and manuals?
- To what extent does KG-based reasoning improve the interpretability and trustworthiness of predictive maintenance systems?
- How can graph-based reasoning support similar-case retrieval and fault diagnosis?
Application Requirements:
The applicant must hold a Bachelor’s degree in a related IT field, have a very good level of mathematics and strong Python programming skills, and must apply to the Master’s program in Data Science. Applicants must also meet the Emerging Researchers Scholarship application requirements.
Application Procedure:
Interested applicants should submit their CV along with all required documents to the following email address:
o.alqawasmeh@psut.edu.jo
For more information about the program, please visit: Emerging Researcher Scholarship
Important Dates:
Deadline for Master’s applications: 5 September 2026
Start of the First Semester 2026/2027: 4 October 2025