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

InteDiag-WTCP er et samarbeidsprosjekt mellom flere universiteter og næringsaktører i Norge og Kina som handler om feilsøking og vedlikehold av kritiske komponenter i landbaserte og havbaserte vindturbiner ved hjelp av digitale teknologier. I 2020 starte to PhD stipendiater hos NTNU i samarbeid med de norske partnere, EDR & MEDESO AS (EDR) and SAFETEC NORDIC AS (SAFETEC). En av de to PhD stipendiatene jobber med å utvikle numeriske metoder og digitale tvillinger av vindturbin drivverk med hovedmål å estimere utmattelse livstid. Andre stipendiaten jobber med data analyses og maskinlæring metoder (f.eks. Neural Network) for vindturbin drivverk tilstandskontroll. I tillegg ble det gjennomført regelmessige møter med de kinesiske partnere og blant annet bruk av måling fra en kinesisk vindpark ble diskutert.

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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.