Monitoring wind turbine gearboxes

TLDR

Wind turbine gearbox reliability is a major concern because repairs are complex, costly, and cause long downtimes, and monitoring has become increasingly important as turbines grow larger and move to remote sites. The paper aims to summarize gearbox failure modes, review SCADA and CMS monitoring methods, present two case studies, and propose future integration of SCADA and CMS data for failure detection, diagnosis, and prediction. The authors review SCADA and CMS monitoring approaches, then apply physics‑based relationships to SCADA data and vibration/debris‑based diagnostics to CMS data in two case studies. The case studies demonstrate that SCADA oil temperature rises and CMS vibration/debris metrics can detect, diagnose, and predict incipient gearbox failures, each method offering distinct strengths.

Abstract

ABSTRACT Concerns amongst wind turbine (WT) operators about gearbox reliability arise from complex repair procedures, high replacement costs and long downtimes leading to revenue losses. Therefore, reliable monitoring for the detection, diagnosis and prediction of such faults are of great concerns to the wind industry. Monitoring of WT gearboxes has gained importance as WTs become larger and move to more inaccessible locations. This paper summarizes typical WT gearbox failure modes and reviews supervisory control and data acquisition (SCADA) and condition monitoring system (CMS) approaches for monitoring them. It then presents two up‐to‐date monitoring case studies, from different manufacturers and types of WT, using SCADA and CMS signals. The first case study, applied to SCADA data, starts from basic laws of physics applied to the gearbox to derive robust relationships between temperature, efficiency, rotational speed and power output. The case study then applies an analysis, based on these simple principles, to working WTs using SCADA oil temperature rises to predict gearbox failure. The second case study focuses on CMS data and derives diagnostic information from gearbox vibration amplitudes and oil debris particle counts against energy production from working WTs. The results from the two case studies show how detection, diagnosis and prediction of incipient gearbox failures can be carried out using SCADA and CMS signals for monitoring although each technique has its particular strengths. It is proposed that in the future, the wind industry should consider integrating WT SCADA and CMS data to detect, diagnose and predict gearbox failures.Copyright © 2012 John Wiley & Sons, Ltd.