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      Probabilistic analysis of gear flank micro-pitting risk in wind turbine gearbox using supervisory control and data acquisition data


      1 , 2 , , 1 , 2 , 3 , 4 , 4

      IET Renewable Power Generation

      The Institution of Engineering and Technology

      probability, gears, wind turbines, SCADA systems, time series, statistical analysis, shafts, lubricants, power engineering computing, power generation control, control engineering computing, pinion gear tooth flank micropitting risk, wind turbine gearbox, supervisory control and data acquisition data, probabilistic risk analysis, WT, SCADA data, gear surface damage, time series, wind speed, generator power, statistical variation, gear shaft torque, random number dataset, rotational speed variation, lubricant film thickness, gear tooth flank contact stress, ISO-TR 15144-1

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          This study investigates the probabilistic risk of gear tooth flank micro-pitting in wind turbine (WT) gearboxes and shows how relatively slow rate of supervisory control and data acquisition (SCADA) data, recorded during operation, can be used to analyse the onset of gear surface damage. Field measured time series of SCADA signals, including wind speed, generator power and rotational speed, were used to obtain the statistical variation of gear shaft torque and rotational speed. From the SCADA data obtained over a 2.2 year period random number datasets of smaller sizes were selected. Based on these random number datasets the effect of gear shaft torque and rotational speed variations on the probabilistic risk of gear micro-pitting was investigated. Determinations of the gear tooth flank contact stress and lubricant film thickness were based on the technical report of gear micro-pitting, ISO/TR 15144-1 (2010). The study has shown that the considered pinion gear is subjected to high load conditions resulting in high contact stresses. The variation of rotational speed causes greater sliding between the gear teeth. The results of specific lubricant film thicknesses have shown that there is considerable risk of gear micro-pitting under the operational conditions recorded from the SCADA field data.

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          Most cited references 18

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          Online wind turbine fault detection through automated SCADA data analysis

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            Wind turbine SCADA alarm analysis for improving reliability

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


                Author and article information

                IET Renewable Power Generation
                IET Renew. Power Gener.
                The Institution of Engineering and Technology
                August 2015
                : 9
                : 6
                : 610-617
                [1 ] Department of Mechanical Engineering, The University of Sheffield , Sheffield, S1 3JD, UK
                [2 ] State Key Laboratory of Mechanical Transmission, Chongqing University , People's Republic of China
                [3 ] School of Engineering and Computing Sciences, Durham University , Durham, DH1 3LE, UK
                [4 ] Design Unit, University of Newcastle , Newcastle, NE1 7RU, UK
                IET-RPG.2014.0277 RPG.2014.0277.R1

                This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)

                Funded by: Engineering and Physical Sciences Research Council
                Award ID: EP/F061811/1
                Research Articles


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