Condition monitoring and lifetime prognosis of wind electrical generators bearings

Research proposals

  • Research area

    Operations and remote autonomous monitoring

  • Institution

    University of Sheffield

  • Research project

    Condition monitoring and lifetime prognosis of wind electrical generators bearings

  • Lead supervisor

    Dr Antonio Griffo (Lecturer – Electrical Engineering, University of Sheffield)

  • Supervisory Team

    Dr Antonio Griffo (Lecturer – Electrical Engineering, University of Sheffield)
    Dr Guang-Jin Li (Senior Lecturer - Electrical Engineering, University of Sheffield)

Project Description:

Reliability and availability are two key requirements in many industrial applications. In the offshore wind power generation, any downtime caused by faults or maintenance results in significant loss of revenues. With the aim of reducing faults and increasing availability, there has been a recent upsurge of interest in real-time monitoring of machine health during its lifetime.

The aims are not only to avoid the risk of catastrophic failures but also to replace costly periodic routine maintenance with condition-based maintenance to be performed only when the remaining useful life (RUL) decreases below a predefined threshold.

The project will investigate novel methodologies for condition monitoring and prognosis of the remaining life of bearings in electrical machines. Based on emerging methodologies for signal processing, sensor fusion and machine learning, the project will develop novel detection methods for bearing faults that combine the available mechanical and electrical signals. It is expected that the approach developed will result in significant improvement in sensitivity to the detection of progressive degradation and incipient faults.

Download all research proposals here