Improved condition monitoring technique for wind turbine gearbox and shaft stress detection. Issue 4 (1st July 2017)

• Record Type: Journal Article
• Authors: Salem, Abdulwahed; Abu‐Siada, Ahmed; Islam, Syed
• Is Part Of: IET science, measurement & technology. Volume 11:Issue 4(2017)
• Page Start: 431
• Page End: 437
• Publication Date: 2017-07-01
• Subjects: condition monitoring -- wind turbines -- gears -- shafts -- stress measurement -- fault diagnosis -- failure (mechanical) -- reliability -- stress analysis -- statistical analysis -- vibrations -- signal detection -- signal resolution -- sensors -- wavelet transforms -- mechanical engineering computing; improved condition monitoring technique -- wind turbine gearbox -- shaft stress detection -- mechanical fault detection -- electrical fault detection -- catastrophic failure -- maintenance cost -- reliability -- gearbox vibration -- shaft torque signatures analyses -- mechanical vibration signal detection -- high‐resolution sensor -- wavelet analyses -- order analyses -- signal processing technique; Measurement -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Periodicals; Nanotechnology -- Periodicals; Electromagnetism -- Periodicals; Medical instruments and apparatus -- Periodicals; 621.3
• DOI: 10.1049/iet-smt.2016.0338
• Languages: English
• ISSNs: 1751-8822

Abstract

Abstract : Condition monitoring has been widely used to detect mechanical and electrical faults of wind turbine in order to avoid any potential catastrophic failures, reduce operational and maintenance cost, and enhance the reliability and availability of the equipment. Although several papers about wind turbine condition monitoring can be found in the literatures, adopting a reliable and cost‐effective technique for wind turbines that usually experience severe mechanical stress due to the harsh weather conditions they are exposed to is still challenging. Although statistical studies show that gearbox failure rate is low, the resulted downtime and the replacement or repairing cost is substantial. This study introduces an improved technique to monitor the condition of the wind turbine gearbox based on gearbox vibration and shaft torque signatures analyses. In this context, a test rig that emulates real wind turbine operation has been developed to analyse the behaviour of wind turbine under various mechanical fault levels. Shaft torque and mechanical vibration signals are detected using high‐resolution sensors and are analysed using two signal processing techniques: wavelet and order analyses to examine the impact of investigated fault levels on the wind turbine drive train. Both signal processing techniques are compared based on their sensitivity to detect incipient fault levels.