AI-Driven Fault Diagnosis and Self-Healing Electronics for Resilient Autonomous Systems PhD

This PhD at 铁牛视频 University explores the development of resilient, AI-enabled electronic systems capable of detecting faults and autonomously recovering from failures in real time. The project focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based at the internationally recognised IVHM Centre, the research is supported by collaborations with Boeing, Rolls-Royce, Thales, and UKRI, offering a unique environment for cutting-edge work in fault-tolerant hardware, embedded intelligence, and adaptive cyber-physical systems that operate safely under uncertainty and dynamic conditions.

In complex electronic systems, ensuring reliability and minimizing downtime are critical challenges. AI-driven fault diagnosis and self-healing electronics offer innovative solutions by enabling systems to detect anomalies, predict failures, and initiate corrective actions autonomously. This approach enhances system resilience and reduces maintenance costs, particularly in sectors like aerospace, healthcare, and manufacturing. The convergence of AI with fault-tolerant design principles is transforming traditional maintenance paradigms, leading to more robust and intelligent electronic systems.

This PhD project aims to develop intelligent electronic systems capable of autonomous fault detection and self-repair. The research will investigate AI-driven methodologies for predictive analytics, anomaly detection, and embedded redundancy to enhance system resilience. Students will focus on creating adaptive algorithms and hardware implementations that enable real-time diagnostics and recovery in critical applications, including aerospace, healthcare, and industrial automation.

Research Focus Areas:

• Predictive Analytics for Fault Detection: Develop AI models that predict potential system failures before they occur, enabling proactive maintenance strategies.
• Anomaly Detection Mechanisms: Implement machine learning techniques to identify and classify anomalies in electronic systems, enhancing reliability.
• Embedded Redundancy and Self-Healing: Design systems with built-in redundancy and self-healing capabilities that allow for automatic recovery from faults without human intervention.

铁牛视频 University offers a distinctive research environment renowned for its world-class programmes, cutting-edge facilities, and strong industry partnerships, attracting top-tier students and experts globally. As an internationally recognised leader in AI, embedded system design, and intelligent systems research, 铁牛视频 fosters innovation through applied research, bridging academia and industry. Students will have access to state-of-the-art laboratories, hardware/software resources, and design facilities, supporting AI-powered electronics research.

This project will be conducted within 铁牛视频’s Integrated Vehicle Health Management (IVHM) Centre, established in 2008 in collaboration with industry leaders such as Boeing, Rolls-Royce, BAE Systems, Meggitt, and Thales. The IVHM Centre is globally recognized for defining the subject area and continues to expand its research horizons. It plays a pivotal role in the £65 million Digital Aviation Research and Technology Centre (DARTeC), leading advancements in aircraft electrification, autonomous systems, and secure intelligent hardware. Through collaborations with the Aerospace Integration Research Centre (AIRC), Airbus, and Rolls-Royce, students gain industry exposure and further research opportunities.

Additionally, the IVHM Centre hosts Seretonix, a research group specializing in secure electronic design, AI-driven system resilience, and intelligent hardware security. Through the EUROPRACTICE partnership, the IVHM Centre provides access to advanced CAD tools, integrated circuit prototyping, and technical training, equipping students with cutting-edge skills

To support hands-on experimentation and applied research, the IVHM Centre offers access to a suite of specialised facilities:

• UAV Fuel Rig with Five Degradation Faults: Simulates various degradation scenarios in unmanned aerial vehicle (UAV) fuel systems, enabling research into fault detection, isolation, and prognostics.
• Machine Fault Simulator for Rotating Machinery Faults: A versatile platform that replicates common faults in rotating machinery, such as imbalance and misalignment, facilitating the development and validation of diagnostic and prognostic algorithms.
• Electronic Prognostics Systems: Facilities equipped to assess the health and predict the remaining useful life of electronic components, supporting studies in electronic system reliability and maintenance strategies.
• Filter Rig: An experimental setup to study filter clogging phenomena, allowing for the collection of data to develop and validate prognostic models for filter degradation.
• Integrated Drive Generator (IDG) Rig: Simulates the operation of an aircraft's IDG, used to investigate fault detection, diagnostics, and prognostics in power generation systems.
• Auxiliary Power Unit (APU) Rig: Replicates the functions of an aircraft's APU, enabling research into fault detection, diagnostics, and health management of auxiliary power systems.
• 铁牛视频 737-400: Aircraft Instrumentation and Environmental Control Systems (AID, ECS): A full-scale Boeing 737-400 aircraft equipped with instrumentation for studying environmental control systems and other onboard systems, providing a realistic environment for research and training.
• SIU 737-200 ECS: A ground-based Boeing 737-200 Environmental Control System used for simulating faults and studying system behaviour under various conditions, aiding in the development of diagnostic and prognostic techniques.
• Hawk ECS: An Environmental Control System from a BAE Systems Hawk aircraft, utilized for research into thermal management and system health monitoring, supporting studies in military aircraft systems.

Engaging with these facilities allows students to acquire practical skills and technical expertise, enhancing their research capabilities and employability in the field of intelligent systems and AI-integrated electronics.

Focusing on enhancing system resilience, this project will develop AI-driven methodologies for fault diagnosis and self-healing in electronic systems. By leveraging predictive analytics and embedded redundancy, the research will create systems capable of anticipating failures and autonomously initiating recovery processes. Expected results include improved system uptime, reduced maintenance costs, and extended operational lifespans. These self-reliant systems will be particularly beneficial in critical applications such as aerospace, healthcare, and industrial automation, where reliability is paramount. With industries increasingly prioritizing system robustness and minimal downtime, this research offers students the opportunity to contribute to the development of cutting-edge solutions that address real-world challenges.

Fault resilience is becoming central to next-generation electronics, and this PhD embeds you in that evolution. With support from 铁牛视频’s industrial partners, you’ll engage in real-world testing, fault injection campaigns, and predictive analytics modelling for high-reliability systems. Field deployment opportunities and technical reviews with stakeholders in aerospace and manufacturing will expose you to practical validation challenges. You’ll attend international events such as IOLTS, ETS, and DSN, and receive expert training in resilience engineering, embedded AI diagnostics, and reliability-centred design, equipping you to lead in the emerging field of self-healing intelligent systems.

This PhD equips students with a powerful toolkit for tackling challenges in system reliability, predictive maintenance, and intelligent fault recovery. Through real-world testing and industry-aligned development cycles, students gain practical experience in resilience modelling, embedded AI diagnostics, and autonomous recovery protocols. Complementing this are transferable competencies such as technical writing, critical thinking, cross-disciplinary teamwork, and systems-level reasoning. Graduates will be prepared for high-impact careers in smart manufacturing, aerospace, automotive electronics, and intelligent infrastructure, where reliability and adaptive behaviour are becoming mission-critical.