Danya Al-Hindawi

Danya Al-Hindawi is a dedicated researcher with a strong academic background in mechanical and electrical engineering. She completed a Bachelor of Engineering (BEng) in Mechanical Engineering at Teesside University between 2020 and 2023, where she developed a deep interest in smart systems, fault diagnosis, and predictive maintenance.

In October 2023, she began her PhD at Teesside University, focusing on the development of advanced diagnostic and prognostic tools for electrical machines. Her research explores the integration of signal processing, artificial intelligence (AI), and digital twin technologies to create efficient and scalable fault detection systems suitable for real-world industrial environments.

Alongside her doctoral studies, Danya worked as a Research Associate on the project “Marine to Electric Power – SeNZe-Tech™ the Smart Way to Clear The Hurdles”. This role involves contributing to innovative solutions in clean energy transitions and intelligent system integration within the marine and power sectors.

Her long-term goal is to advance the field of smart maintenance technologies, particularly in the application of AI-driven digital twins for sustainable and intelligent industry systems.

• Fault Diagnosis and Prognosis in electrical machines
• AI-based prognostic tools for real-time condition monitoring
• Application of deep learning and transfer learning in smart diagnostics
• Signal processing techniques (time, frequency, and time-frequency domains) for fault feature extraction.
• Digital twin technologies for predictive maintenance.
• Intelligent and scalable solutions for industrial automation and energy systems.
• Cross-domain generalisation of diagnostic and prognostic models across machine types.

• Research Associate, Marine to Electric Power – SeNZe-Tech™ the Smart Way to Clear The Hurdles
  Supporting clean energy transition initiatives and the integration of intelligent monitoring systems in marine and power applications.
• Conception X Cohort Member
  Participated in the deep tech venture program, exploring commercial applications of PhD research and entrepreneurial pathways in smart maintenance technologies.
• Listed Reviewer, ECCE Europe Conference
  Selected as a technical reviewer for the ECCE Europe Conference, contributing to the reviewer pool for papers in the field of electrical machines and power systems.
• Poster Presentation – Best in School, School of Computing, Engineering & Digital Technologies, ExpoTees 2023
  Recognised for the best poster presentation within the school at Teesside University’s annual graduate showcase.
• IMechE University Institution Project Award
  Presented by the Institution of Mechanical Engineers in recognition of excellence in undergraduate project work.