Abstract:
Prognostic health management is an effective approach to enhance the reliability and reduce downtime of rotating machines, such as wind turbines. To achieve effective hea...Show MoreMetadata
Abstract:
Prognostic health management is an effective approach to enhance the reliability and reduce downtime of rotating machines, such as wind turbines. To achieve effective health management, fault prognosis is indispensable. This paper proposes a fault prognostic method for drivetrain gearboxes, which are one of the most troublesome subassemblies in wind turbines. The proposed method consists of signal collection, health index extraction, health index prediction, and decision making for maintenance. The signal used for fault prognosis is a phase current measured from the generator connected with the gearbox. A health index called noise-to-signal ratio (NSR) of the current signal is proposed to reflect the health condition of the gearbox. A recurrent neural network (RNN) is designed to predict the health index online and maintenance is scheduled when the predicted health index reaches a predetermined threshold. The proposed fault prognostic method is validated by the data obtained from an accelerated gearbox run-to-failure experiment for an emulated wind turbine drivetrain consisting of a gearbox and a generator.
Date of Conference: 14-17 May 2017
Date Added to IEEE Xplore: 02 October 2017
ISBN Information:
Electronic ISSN: 2154-0373