CPL - Chalmers Publication Library
| Utbildning | Forskning | Styrkeområden | Om Chalmers | In English In English Ej inloggad.

An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings

Pramod Bangalore (Institutionen för energi och miljö, Elteknik ; Svenskt VindkraftsTekniskt Centrum (SWPTC)) ; Lina Bertling
IEEE Transactions on Smart Grid (1949-3053). Vol. 6 (2015), 2, p. 980-987.
[Artikel, refereegranskad vetenskaplig]

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.

Nyckelord: Artificial neural networks (ANN), condition monitoring system (CMS), maintenance management, smart grid, supervisory control and data acquisition systems (SCADAs), wind power generation.

Den här publikationen ingår i följande styrkeområden:

Läs mer om Chalmers styrkeområden  

Denna post skapades 2015-02-02. Senast ändrad 2015-05-29.
CPL Pubid: 211933


Läs direkt!

Länk till annan sajt (kan kräva inloggning)