Early Detection of Extreme Engine Events (EDE3)

Research project predicts serious engine failures that lead to breakdown

Duration of the project

1.8.2025 – 31.7.2027

Source of funding

Business Finland

Total funding

1 029 998 €

The EDE3 project aims to develop a comprehensive framework for the early detection, prediction, and prevention of severe engine failures, with a focus on large internal combustion engines. Bringing together at Turku University of Applied Sciences and industry leaders like Wärtsilä, AGCO Power, Nome, Unikie, and EDR&Medeso, the initiative combines high-frequency monitoring, advanced simulation modeling, and real-time data analysis. As part of Business Finland’s Co-Research efforts and the WISE Veturi Project, EDE3 leverages controlled engine tests, digital twin technology, and adaptive diagnostics to enhance reliability and safety across a wide range of heavy machinery.

The EDE3 (Early Detection of Extreme Engine Events) project aims to develop a comprehensive framework for the real-time detection, The EDE3 (Early Detection of Extreme Engine Events) project seeks to develop a framework for real-time identification, prediction, and prevention of catastrophic failures in large mechanical engines, particularly internal combustion engines. Led by Turku University of Applied Sciences in collaboration with industry partners such as Wärtsilä, AGCO Power, Nome, Unikie, and EDR&Medeso, the project integrates high-frequency monitoring, advanced fault-simulation modeling, and adaptive signal processing. EDE3 is a Business Finland Co-Research project contributing to Wärtsilä’s Energy Business (WISE Veturi Project).

The EDE3 approach combines experimental engine testing, physics-based modeling, and real-time data analysis to detect engine faults earlier and with greater accuracy than existing systems.

The project is structured into four main work packages:

• experimental engine testing,
• model development,
• process development (scaling solutions to larger engines and new technologies), and
• project management.

Notably, the project leverages destructive engine testing to generate rare failure data, which is essential for validating new predictive diagnostics models.

The methods and frameworks developed are designed to be broadly applicable across various heavy machinery

The anticipated outcomes include enhanced engine reliability, reduced operational risks, improved maintenance practices, and the creation of commercial solutions for predictive diagnostics. The methods and frameworks developed are designed to be broadly applicable across various heavy machinery, positioning the Finnish consortium at the forefront of industrial reliability engineering.