An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings
Artikel i vetenskaplig tidskrift, 2015

Gearbox has proven to be a major contributor toward downtime in wind turbines. The majority of failures in the gearbox originate from the gearbox bearings. An early indication of possible wear and tear in the gearbox bearings may be used for effective predictive maintenance, thereby reducing the overall cost of maintenance. This paper introduces a self-evolving maintenance scheduler framework for maintenance management of wind turbines. Furthermore, an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed. The ANN-based condition monitoring approach is applied to gearbox bearings with real data from onshore wind turbines, rated 2 MW, and located in the south of Sweden. The results demonstrate that the proposed ANN-based condition monitoring approach is capable of indicating severe damage in the components being monitored in advance.

Artificial neural networks (ANN)

supervisory control and data acquisition systems (SCADAs)

maintenance management

wind power generation.

condition monitoring system (CMS)

smart grid

Författare

Pramod Bangalore

Svenskt VindkraftsTekniskt Centrum (SWPTC)

Chalmers, Energi och miljö, Elkraftteknik

Lina Bertling Tjernberg

The Royal Institute of Technology (KTH)

IEEE Transactions on Smart Grid

1949-3053 (ISSN)

Vol. 6 980-987

Drivkrafter

Hållbar utveckling

Styrkeområden

Energi

Ämneskategorier

Elektroteknik och elektronik

DOI

10.1109/TSG.2014.2386305