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IKTPLUSS-IKT og digital innovasjon

Perception & Fusion of Multidimensional Information & Cooperative Decision-making for Intelligent Diagnosis of Wind Turbine Critical Parts

Alternative title: Flerdimensjonale informasjon og kooperativ beslutningstaking systemer for intelligent vedlikehold av kritiske komponenter i vind turbiner

Awarded: NOK 8.4 mill.

InteDiag was a university-dominated, industry-assisted and competence-complementary collaborative project between Norway and China with focus on structural health/condition monitoring and fault detection/diagnosis for onshore and offshore wind turbine components using digital technologies. The project was conducted in the period of 2020-2023, with two full time PhD candidates at NTNU working with the Norwegian partners EDR & MEDESO AS (EDR) and SAFETEC NORDIC AS (SAFETEC). One PhD has been working on the development on a digital twin for wind turbine drivetrain where a methodology was developed and validated to estimate the drivetrain loads using vibration and SCADA data, for the purpose of online fatigue monitoring. The second PhD has been working on the application of machine-learning methods for gearbox bearing damage and fault detection and diagnosis, also innovative methods for damage modelling. The results of the project have made path towards developing online physical and data driven digital twins not only for wind turbines but also for other rotating machinery.

The InteDiag project has contributed in developing theories, methods and models for wind turbine intelligent fault detection/diagnosis and structural health management. A demonstration platform that is composed of real and virtual parts through a digital twin model has been developed, to test and validate wind turbine intelligent operation and maintenance scheme. Potential impacts include key technologies for wind turbine multi-physics measurement, sensor layout optimization and networking transmission, parameter soft measurement, data quality assessment, by which it can address the issues of insufficient perceived information on wind turbine critical components and create the knowledge for understanding the multi-source heterogeneous data and their correlation under normal and fault conditions. The project has the benefit to the future value creation in the wind industry with a potential to reduce the levelized cost of energy.

This is a collaborative project between Norway and China. The Norwegian team is led by Norwegian University of Science and Technology (NTNU), in collaboration with two monitoring and digital service companies, EDR & MEDESO AS (EDR) and SAFETEC NORDIC AS (SAFETEC). The Chinese team is led by Hunan University (HNU), in collaboration with Central South University (CSU), Hunan University of Science and Technology (HUST) and two leading wind power companies XEMC WINDPOWER (XEMC) and GOLDWIND TECHNOLOGY (GOLDWIND). The goal is to use digital and intelligent solutions for structural health/condition monitoring to address the issues of high failure rate and high operation/maintenance cost of wind turbines. To achieve this goal, a "perception-transmission-processing-utilization-control" digital solution will be proposed for operation/maintenance of wind turbine critical components and an advanced visualization platform, which supports data exchange and sharing among various parts, for wind turbine monitoring and fault diagnosis will be developed and demonstrated. The main activities and novelties are summarized as below: - A methodological system for global information perception via measurements is proposed for the full life circle information of the wind turbine critical components (blades, bearings and gearbox). - A framework combining deep fusion and automatic acquisition is proposed to fuse multi-source heterogeneous data and acquire field knowledge. - Intelligent fault detection/diagnosis methods for wind turbine critical components are proposed and applied using simulated data from numerical models and measurement data. - A strategy for hierarchical early warning and fault tracing is proposed based on coordinated decision-making using digital twin technologies and demonstrated in actual wind farms.

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IKTPLUSS-IKT og digital innovasjon

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