PSI - Issue 14

Available online at www.sciencedirect.com Structural Integrity Procedia 00 (2018) 000–000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2018) 000–000 Available online at www.sciencedirect.com ScienceDirect

www.elsevier.com/locate/procedia www.elsevier.com/locate/procedia

ScienceDirect

Procedia Structural Integrity 14 (2019) 937–944

2nd International Conference on Structural Integrity and Exhibition 2018 Integrated Vibro-Acoustic Analysis and Empirical Mode Decomposition for Fault Diagnosis of Gears in a Wind Turbine Vamsi Inturi a* , Sabareesh G R a , Vaibhav Sharma a a Department of Mechanical Engineering, BITS Pilani, Hyderabad Campus, Telangana, India - 500078 Abstract Over the last few years, wind turbine technology has experienced a rapid growth among the other renewable power developing technologies with respect to market share, size and technological design. Generally, wind turbines are subjected to harsh operating conditions which further yields to damage of the critical components. Hence, health monitoring of key components is a vital task which predicts the damage severity and gives the flexibility to plan the maintenance tasks. Wind turbine condition monitoring is a major area of interest in recent years aiming to improve the life of the machine components simultaneously reducing the operational and maintenance cost. Gearbox in wind turbines has the largest share of downtime among all other components affecting directly the cost of operation and maintenance. In this current investigation, an attempt has been made to diagnose the gear faults by using Empirical Mode Decomposition (EMD) methodology. Two condition monitoring techniques such as vibration analysis and acoustic signal analysis are integrated and the experiments are performed on a laboratory scaled three-stage gearbox having the speed ratio of 48:1. Local gear faults such as tooth chip and tooth root crack are seeded and the response is recorded in the form of vibration and acoustic signals. EMD analysis is implemented and the statistical features are extracted from the acquired data. The representative features are identified using a decision tree algorithm and these are classified using pattern recognition techniques – Support Vector Machine (SVM) to distinguish between the healthy and faulty classes. The challenges and the potential advantages are also discussed in this paper to establish the focus of integrated condition monitoring systems. © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers. 2nd International Conference on Structural Integrity and Exhibition 2018 Integrated Vibro-Acoustic Analysis and Empirical Mode Decomposition for Fault Diagnosis of Gears in a Wind Turbine Vamsi Inturi a* , Sabareesh G R a , Vaibhav Sharma a a Department of Mechanical Engineering, BITS Pilani, Hyderabad Campus, Telangana, India - 500078 Abstract Over the la t few years, wind turbine technology has experienced rapid growth among the othe renew ble power eveloping technolo ies with respe t to ma ket share, size and technologi al desig . Generally, wind turbines are subjected to harsh opera ing conditions which further yi lds to damage of the critical components. Hence, health monitoring of key compone ts s a vital ask which predicts the damag seve ity and gives the flexibility to plan the mainten nce tasks. Wi d urbine condition monitoring is a maj r rea of inter st in recent y s aiming to improv t e lif of the machine components simult ne usly reduci g the operational and maintenance cost. Gearbox i wi d turbines has the largest share of downtime among all other components affecting directly the cost of operatio and mainten nce. In this current investigati n, an atte pt has been made to diagnose the gear f ults by using Emp ric Mode Decomposition (EMD) methodology. Two c ndition monitoring techniques such as vibration an lysis and acoustic signal analysis re integr ted and the experiments are perfo med on a laboratory scaled th e-stag gearb x having the speed ratio of 48:1. Local gear fau ts such as tooth chip and tooth root crack are seeded nd the response is recorded in the form of vibration a d acoustic sign ls. EMD analy s is implement d nd the st tistical fe tures are xtracted from the acqu red data. The representativ features ar identified using a decision tr e algorithm and these are classified using pattern recogniti n techniques – Support Vector Machine (SVM) to distingu between the healthy and faulty classes. The challenges and the potential advantages are also discussed in this paper to establish the focus of integrated condition monitoring systems. © 2018 The Authors. Published by Elsevier B.V. This is a open access article und r the CC BY-NC-ND lic nse (https://creat vecommons.org/licenses/by- c-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers. Keywords: Wind turbine gearbox; Condition monitoring; Vibration analysis; Acoustic signal analysis; Empirical mode decomposition. Keywords: Wind turbine gearbox; Condition monitoring; Vibration analysis; Acoustic signal analysis; Empirical mode decomposition.

*Corresponding author. Tel.: +91-8977767713 E-mail address: p20160025@hyderabad.bits-pilani.ac.in *Corresponding author. Tel.: +91-8977767713 E-mail address: p20160025@hyderabad.bits-pilani.ac.in

2452-3216 © 2018 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers. 2452-3216 © 2018 The Authors. Published by Elsevier B.V. This is a open access article und r the CC BY-NC-ND lic nse (https://creat vecommons.org/licenses/by- c-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers.

2452-3216  2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) Selection and peer-review under responsibility of Peer-review under responsibility of the SICE 2018 organizers. 10.1016/j.prostr.2019.07.074