MSc Thesis Defense Announcement of Joseph El-Ghaname:"Automotive Fault Detection using Knowledge Graph Embedding "

Date: Tuesday, May 24, 2022 

Time:  10:30 am – 12:30 pm 

Meeting URL:    https://us06web.zoom.us/j/86433678647?from=addon 

Passcode: If interested in attending this event, contact the Graduate Secretary at csgradinfo@uwindsor.ca with sufficient notice before the event to obtain the passcode

Automotive industry has witnessed expensive, dangerous, and potentially fatal recalls over the years. With the rollout of new electric vehicles (EV), additional complexity is brought into the manufacturing process. Companies must use expensive fault detection systems to identify defects in the assembly process. Knowledge-based approaches for fault detection in manufacturing and assembly systems reflect modern practices in Industry 4.0. In this thesis, we examine the feasibility of using knowledge graphs (KG) for their effectiveness in detecting faults in a custom dataset. We implement a KG Completion (KGC) algorithm and compare different KG Embedding (KGE) models. Furthermore, we measure and compare the Mean Reciprocal Rank (MRR) and Hits@k to evaluate the algorithm based on various KGE approaches and models. Our findings from our experiments and thesis paves a new pathway for vehicle manufacturers and car makers, allowing for a feasible and comprehensive fault detection system and framework. By combining state-of-the-art KGE models and a first-hand case study involving an electric vehicle knowledge graph dataset (EV-KG), this thesis solidifies future KG-related fault detection research in the field and opens numerous opportunities for further development and application in the real-world industry.  

 

Keywords: Knowledge Graphs, Knowledge Graph Completion, Knowledge Graph Embedding, fault detection, link prediction.

 

Internal Reader: Dr. Pooya Moradian Zadeh         

External Reader: Dr. Narayan Kar 

Advisor: Dr. Ziad Kobti 

Chair: Dr. Dan Wu