About me

My current research is a superposition between Computational Applied Physics and Maintenance Engineering (Physics-of-Failures) such as Hybrid models (ISO-13381-1 2025): Knowledge-based or Physics-based modelling and condition monitoring for Swedish Transport Administration (Project: Optimal rail topography via Artificial Neural Network) and EU's rail (Academics4rail).
I am currently PhD candidate in the research field of Maintenance in Railway applications with focus on implementing novel algorithm paradigm shifts such as Physics-Informed Machine Learning (PIML), Scientific ML and introduce new System of Thinking such as Intelligent Maintenance Workflow for the Railway.
The ultimate goal is to implement a Physics-based models such PIML as Proof-of-concept (PoF) —> Technology Readiness Level 3 (TRL) and then move forward to in-situ measurement and practical experience (TRL5-6) with the support of Trafikverket (TRV) and/or Academics4rail partners to inspect, monitor and assess condition of a railway asset.

My journey began in Lund (Skåne, Sweden) in the heavy fluid pump industries where I worked in Quality Control and Testing room as engineer (professional certification). This experience gave me lots inspiration and motivation to investigate the reasons of Physics-of-Failures in mechanical pumps. This challenges was the driven force for deeper study in Materials Science, Physics at Lund University while working and now pursuing a PhD in Maintenance Engineering: Railway, Prognostics Health Management (PHM) and Hybrid modelling —> Physics-based: PIML, PINN or more widely Knowledge-based + AI.

My background is in Materials Science and Neuromorphic Nanophotonics Sensing Technology at Lund University in Physics. Under the Horizon Europe project: Insect-Brain inspired Neuromorphic Nanophotonics , on Artificial Neurons made of Semiconductor Nanowires Master thesisand Nanophotonics article: Optical broadcasting Neural Network connectivity. The main applications of Neuromorphic sensing devices are from Robotics , Event-triggered sensors for Condition Monitoring on assets or components to Medical photo-detector for cancer mapping, for instance. Neuromorphic architecture and algorithms are particular appealing for theirs low-latency response in real-time applications and low energy consumption.

RESEARCH OBJECTIVES:

• Develop and implement an autonomous framework for anomaly detection and predictive degradation analysis of railway assets to facilitate the transition from reactive to proactive (prescriptive) maintenance strategies.

• Deliver a decision-support framework and tools integrating Hybrid models (ISO 13381-1-2025 + Academics4rail subtask WP8) e.g. Physics-Informed Machine Learning (Physics-based) or Knowledge-based with Prognostics and Health Management (PHM) methodologies to optimise maintenance planning and resource allocation.

Subtasks

• Requirements for data generation: definition of the needs, specifications, and requirements of a Use Case (UC) from the relevant stakeholders of railway, such as, type of asset, location of the asset, boundary conditions. KPIs will also be defined to measure the impact of the developed system for asset maintenance decision framework for PHM. 

• Hybrid Prognostic modelling:  The goal of this task will be to develop hybrid methods for predictive maintenance by integrating data-driven (using Machine Learning approaches) and physical models. 

• On-site Monitoring and inspection of assets: Data related to the asset will be collected from the selected UC location. Data is collected from measurements of the asset status and several other data sources, such as, failure data, measurement data, operational data, weather data, and asset registry. Plus, data mining on collected data.

RESEARCH QUESTIONS:

RQ1 Can a half-cylinder-on-flat contact model be enforced into the study of a wheel-rail root cause analysis as a proof of concept (PoC)? 

RQ2: How does a physics-based simulation framework and scientific ML facilitate a cognitive maintenance workflow to support human decisions in the railway?