Wind Turbine Gearbox Anomaly Detection Based on Adaptive Threshold and Twin Support Vector Machines

• Published in: IEEE transactions on energy conversion Vol. 36; no. 4; pp. 3462 - 3469
• Main Authors: Dhiman, Harsh, Deb, Dipankar, Muyeen, S. M., Kamwa, Innocent
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.12.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive threshold; Anomalies; Anomaly detection; condition monitoring; Decision trees; Downtime; Failure; False alarms; Fault detection; Fault diagnosis; Gearboxes; Machinery condition monitoring; Multilayers; neural network; Neural networks; Reliability aspects; SCADA; Support vector machines; Temperature distribution; Training; Turbines; Wind power; Wind turbines
• DOI: 10.1109/TEC.2021.3075897
• ISSN: 0885-8969

Data-driven condition monitoring reduces downtime of wind turbines and increases reliability. Wind turbine operation and maintenance (O&M) cost is a significant factor that calls for automated fault detection systems in wind turbines. In this manuscript, the anomaly detection problem for wind turbine gearbox is formulated based on adaptive threshold and twin support vector machine (TWSVM). In this work, SCADA data from wind farms located in the U.K. is considered with samples from twelve months before failure, and from one month before failure. Gearbox oil and bearing temperatures are used as two univariate time-series for analyzing adaptive threshold. The effectiveness of the proposed method is compared with standard classifiers like support vector machines (SVM), k-nearest neighbors (KNN), multi-layer perceptron neural network (MLPNN), and decision tree (DT). Anomaly detection of wind turbine gearbox using TWSVM and adaptive threshold results in an accurate performance, thus increasing the reliability. The missed failure and false positive rate that indicate the proposed methodology's ability is also investigated to discriminate between false alarms, and comparison with previous studies shows superior performance.