About this role
Thesis Worker 30 hp - Integrating Wear and Sensor information
30 hp – Integrating Wear and Sensor information to predict Remaining Useful Life using Data-driven methods
Introduction
Thesis work is an excellent way to get closer to Scania and build relationships for the future. Many of today's employees began their Scania career with their degree project.
Background
TRATON GROUP is one of the world’s leading commercial vehicle manufacturers, with brands including Scania, MAN, International and Volkswagen Truck & Bus. Its portfolio covers light commercial vehicles, trucks and buses, complemented by financing, charging and digital logistics services. Through its global operations, production sites and extensive sales and service networks, TRATON has access to diverse vehicle platforms, real-world operational data and fleet deployment environments. This provides a strong foundation for developing and validating innovative solutions for sustainable and efficient transportation.
In the Cloud and Embedded Plattform domain within TRATON, we develop new solutions for connected vehicles in our Internet of Things (IoT) platform, as part of TRATON’s increasing focus on communication, services and smart transport solutions. Advanced data analysis capabilities are a cornerstone enabler in this development.
Target/scope
Modern connected vehicles and industrial systems are increasingly equipped with sensors that continuously monitor their condition. This enables a shift from reactive to predictive maintenance, where decisions are based on the expected Remaining Useful Life (RUL) of components.
RUL prediction relies on two complementary information sources: wear factors, which describe accumulated degradation through usage and operating conditions, and sensor factors, which reflect the current observed state of the system.
Our recent work proposes a probabilistic framework that combines these two sources using a survival-based aging model and a data-driven model, enabling not only RUL estimation but also associated prediction uncertainty.
The objective of this thesis is to implement and extend this framework, and to investigate how different modelling and uncertainty quantification choices affect RUL prediction performance and reliability.
If time permits, investigate extensions that relax the proxy assumption used in the data-generating formulation, with the aim of developing a more general relationship between wear, latent component health, sensor observations, and failure.
The thesis provides an opportunity to work at the intersection of machine learning, survival analysis, uncertainty quantification and predictive maintenance, with both methodological research questions and applications to real-world industrial systems.
Reference
Srinivasan, Abhishek, et al. "Integrating Survival-Based Aging Models with Data-Driven RUL Prognostics." PHM Society European Conference. Vol. 9. No. 1. 2026.
Education/line/direction
Masters programmes in Machine Learning, Data Science, Computer Science, Complex Adaptive Systems or similar. Number of students: 1 Start date for the Thesis project: Spring 2027 Estimated timescale: 20 weeks
Contact person and supervisor
Abhishek Srinivasan, Data Scientist Juan Carlos Andresen, Group Manager