Side‐band algorithm for automatic wind turbine gearbox fault detection and diagnosis

Abstract

Publisher's copyright statement:This paper is a postprint of a paper submitted to and accepted for publication in IET renewable power generation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital Library.Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractImproving the availability of wind turbines (WT) is critical to minimising the cost of wind energy, especially offshore. The development of reliable and cost-effective gearbox condition monitoring systems (CMS) is of concern to the wind industry, because gearbox downtime has a significant impact on WT availabilities. Timely detection and diagnosis of developing gear defects is essential to minimise unplanned downtime. One of the main limitations of most current CMSs is the timeconsuming and costly manual handling of large amounts of monitoring data, therefore automated algorithms would be welcome. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. Based on experimental evidence from the Durham condition monitoring test rig, a gear condition indicator was proposed to evaluate the gear damage during non-stationary load and speed operating conditions. The performance of the proposed technique was then successfully tested on signals from a full-size WT gearbox that had sustained gear damage, and had been studied in a National Renewable Energy Laboratory's (NREL)programme. Results show that the proposed technique proves efficient and reliable for detecting gear damage. Once implemented into WT CMSs, this algorithm can automate data interpretation, reducing the quantity of information that WT operators must handle.

Automated on‐line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition

“…These features are usually studied in either the time or frequency domains. Some typical examples for detecting incipient WT gearbox failures have been developed using CMS signal analysis by Crabtree [12], SCADA signal variance analysis by Feng et al [13] and an automatic CMS Sideband Power Factor (SBPF) algorithm by Zappala et al [14].…”

Investigation of induction generator wide band vibration monitoring using fibre Bragg grating accelerometers

“…Therefore, a vibration based CMS should not rely on changes in a single frequency component for effective bearing fault detection. An algorithm that simultaneously monitors multiple fault related frequency components would provide increased diagnostic reliability for bearing fault detection, particularly when incipient bearing faults are concerned [25]. In order to monitor all the fault frequencies of interest, an accelerometer with satisfactory wide band sensitivity would be desirable.…”

Research on Misalignment Fault Isolation of Wind Turbines Based on the Mixed-Domain Features

“…The operation and maintenance cost of up to 25-30% seriously restrict the development of wind power industry [1,2]. Therefore, how to reduce the risk of wind turbine operation and decrease the cost of wind power generation has become a research topic for many scholars.…”