ANFIS Models for Fault Detection and Isolation in the Drive Train of a Wind Turbine

Zakaria Zemali, Lakhmissi Cherroun, Nadji Hadroug, Ahmed Hafaifa


The paper aims to improve the fault detection and isolation process in wind turbine systems by developing intelligent systems that can effectively identify and isolate faults. Specifically, the paper focuses on the drive train part of a horizontal axis wind turbine machine. The proposed fault diagnostic strategy is designed using an adaptive neural fuzzy inference system (ANFIS), which is a type of artificial neural network that combines the advantages of both fuzzy logic and neural networks. The ANFIS is used to generate residuals that occur after faults have been detected, and to determine the appropriate thresholds needed to correctly detect faults. The simulation results show that the proposed fault diagnostic strategy is effective in detecting faults in the drive train part of the wind turbine system. By using intelligent systems such as ANFIS, the fault detection process can be automated and streamlined, potentially reducing maintenance costs and improving the overall performance and efficiency of wind turbine systems.


Wind Turbine, Drive Train, Fault Detection, ANFIS, Residual, Estimation.

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Z. Gao, X. Liu, “An Overview on Fault Diagnosis, Prognosis and Resilient Control for Wind Turbine Systems,” Processes, vol. 9, 300. 2021, pr9020300

L. Zepeng, L. Zhang, “A review of failure modes, condition monitoring and fault diagnosis methods for large-scale wind turbine bearings,” Measurement, vol. 149, 2020, pp. 107002

F. P. Marquez, C. Quiterio Gómez Muñoz, “A New Approach for Fault Detection, Location and Diagnosis by Ultrasonic Testing,” Energies, vol. 13, no. 5, 2020, pp. 1192;

[M. S. Li, D. YD. Yu, Z. M. Chen, K. S. Xiahou, T. Y. Ji.Q. H. Wu, “A Data-Driven Residual-Based Method for Fault Diagnosis and Isolation in Wind Turbines,” IEEE Transactions on Sustainable Energy, Vol.10, no 2, 2019.

Shen Yin Guang Wang Hamid Resa Karim, “Data-driven design of robust fault detection system for wind turbines,” Mechatronics, vol. 24, no. 4, 2014, pp. 298-306

P. S. Odgaard, and M. Kinnaert, “Fault-tolerant control of wind turbines: A benchmark model,” IEEE Transactions on Control Systems Technology. Vol. 21, no 4, 2013, pp. 1168–1182.

S. Simani and P. Castaldi, “Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System,” Appl. Sci., vol 9, no. 4, 2019, pp. 783,

S. Cho, M. Choi, Z. Geo and T. Moan, Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks Renewable Energy

Z. Zemali, L. Cherroun, A. Hafaifa and N. Hadroug, Fault Diagnosis Structure based on Kalman Filter for the Pitch System of a Wind Turbine Process, 2nd Algerian Symposium on Renewable Energy and Materials ASREM2022, March 16-17, 2022, Medea Algeria.

H. Wang, H. Wang, G. Jiang, Y. Wang, S. Ren, ''A multiscale spatio temporal convolutional deep belief network for sensor fault detection of wind turbine,'' Sensors, vol. 20, no. 12, 2020, pp. 1–14.

Y. Chang, J. Chen, C. Qu, T. Pan, “Intelligent fault diagnosis of Wind Turbines via a Deep Learning Network Using Parallel Convolution Layers with Multi-Scale Kernels,” Renewable Energy, vol. 135, 2020, pp. 205-21.

Y. Fu, Z. Gao, Y. Liu, A. Zhang, X. Yin, Actuator and sensor fault classification for wind turbine systems based on fast Fourier transform and uncorrelated multi-linear principal component analysis techniques, Processes, vol. 8, no. 9, 2020, pp. 1066

Y. Fu, Y. Liu, Z. Gao, Fault classification in wind turbines using principal component analysis technique, in: IEEE 17th International Conference on Industrial Informatics (INDIN),, IEEE, 2019, pp. 1303e1308.

N. Laouti, S. Othman, M. Alamir, N. S. Othman. “Combination of model-based observer and support vector machines for fault detection of wind turbines; “International Journal of Automation and Computing, vol. 11, 2014, pp. 274-287.

A. Saci, L. Cherroun, O. Mansour and A. Hafaifa, "Effective Fault Diagnosis Method for the Pitch System of a Wind Turbine", First International Conference on Renewable Energy Advanced Technologies and Applications (ICREATA’21), October 2021, Adrar-Algeria. ISBN: 978-9931-9819-0-9

A. Saci, L. Cherroun, A. Hafaifa and O. Mansour, “Effective Fault Diagnosis Method for the Pitch System, Drive Train and the Generator with Converter in a Wind Turbine System,” Electrical Engineering. Vol. 104, no. 4, 2022, pp. 1967-1983,

E. Jesús Pérez, F. López-Estrada, V. Puig, G. V. Palomo, I. Santos-Ruiz, Fault diagnosis in wind turbines based on ANFIS and Takagi–Sugeno interval observers, Expert Systems With Applications,

P.F. Odgaard, and Kinnaert, M, “Fault tolerant control of wind turbines- a benchmark model.” 7th IFAC symposium on fault detection, supervision and safety of technical processes, 500, 155-160.

J. S. R. Jang. ANFIS: adaptive network based fuzzy inference systems. IEEE Transactions on Syst Man Cybern., vol. 23, no.5, 1993, pp. 665-685.

L. Cherroun, N. Hadroug, M. Boumehraz, “Hybrid Approach Based on ANFIS Models for Intelligent Fault Diagnosis in Industrial Actuator,” Journal of Electrical and Control Engineering, vol.3, no.4, 2013, pp. 17-22.

L. Cherroun and M. Boumehraz, "Path Following Behavior for an Autonomous Mobile Robot using Neuro-Fuzzy Controller", International Journal of Systems Assurance Engineering and Management, (IJSA), Springer-Verlag, vol. 5, no. 3, 2014, pp. 352-360.



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