Sideband algorithm for automatic wind turbine gearbox fault detection and diagnosis

Abstract

Improving the availability of wind turbines (WT) is critical to minimise the cost of wind energy, especially for offshore installations. Because gearbox downtime has a significant impact on WT availabilities, the development of reliable and cost-effective gearbox condition monitoring systems (CMSs) is of great concern to the wind industry. Timely detection and diagnosis of developing gear defects within a gearbox is an essential part of minimizing unplanned downtime of wind turbines. Monitoring signals from WT gearboxes are highly non-stationary because turbine load and speed vary continuously with time. Manual handling of large amounts of monitoring data is time-consuming and costly, and is one of the main limitations of most current CMSs. Therefore, automated algorithms are required. This paper presents a fault detection algorithm for incorporation into a commercial CMS for automatic gear fault detection and diagnosis. Based on the experimental evidence from the Durham condition monitoring test rig, a gear condition indicator has been proposed to evaluate the gear damage during non-stationary load and speed operating conditions. The algorithm allows the assessment of gear fault severity by tracking progressive tooth gear damage during variable speed and load operating conditions of the test rig. The performance of the proposed technique has then been successfully tested on signals from a field test of a full-size wind turbine gearbox that has sustained gear damage. 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.