Contact Burak Suslu
Background
I earned a BEng in Electrical and Electronics Engineering from Erciyes University, Turkey, in 2017. Immediately afterwards, I founded SSL Elektrik-Elektronik, an engineering consultancy in Batman, where I have since delivered a portfolio of automation and high-voltage energy-distribution projects for state-run and private oil-and-gas clients. To deepen my technical expertise, I completed an MSc (Merit) in Advanced Electrical and Electronic Engineering at Brunel University London in 2019, and I am now in the final write-up phase of a sponsored PhD in Transport Systems at Cranfield University, researching sensor-optimisation methods for next-generation aircraft health-management systems.
Research opportunities
My work sits at the intersection of sensor technology, optimisation, and intelligent asset management for safety-critical aerospace and energy applications. Key interests include
- Sensor-Optimised Health Management – designing minimal yet information-rich sensor suites for condition-based maintenance and remaining-useful-life prediction of complex subsystems (engine, electrical power, fuel, and environmental-control systems).
- Physics-informed & Data-driven Methods – combining first-principles models with machine-learning (ANN / Gaussian-process) surrogates to accelerate high-fidelity simulations and uncertainty propagation.
- Multi-Objective Optimisation & Decision Support – advancing the NDCI-MOSOF framework to balance cost, observability, and fault-detection performance when allocating sensors in tightly-coupled systems.
- Robust Automation & Power Distribution – leveraging FPGA-based control, AI-enhanced automation, and high-voltage design to improve reliability in oil-and-gas transmission networks.
- Emerging Technologies – tracking quantum-safe cryptography, 4D-printed sensing materials, and generative AI for rapid concept visualisation and human–machine interfaces.
Current activities
At Cranfield University’s IVHM Centre, I am developing and rigorously benchmarking sensor-optimisation algorithms that pinpoint the smallest, highest-value sensor suites—balancing information gain, cost, reliability, and weight—to enable robust diagnostics and prognostics in commercial and civil aircraft health-management systems.