Failure Diagnosis of Wind Turbine Bearing Using Feature Extraction and a Neuro-Fuzzy Inference System (ANFIS)
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Failure diagnosis, Feature extraction, Neuro fuzzy
Bearing failures are the most common type of malfunction in wind turbines. As such, isolating these defects enables maintenance scheduling in advance; hence, preventing further damage to turbines. This paper introduces a new fault detection and diagnosis (FDD) method to isolate two types of bearing failures in Wind turbines (WTs). The proposed FDD method consists of a feature extraction/feature selection and an adaptive neuro-fuzzy inference system (ANFIS) method. The feature extraction and selection phase identifies proper features to capture the nonlinear dynamics of the failure. Then, the ANFIS classifier diagnoses the failure type using the extracted features. Several experimental test studies with the historical data of wind farms in South-western Ontario are performed to evaluate the performance of the FDD system. Test results indicate that the proposed monitoring system is accurate and effective.
Kordestani, Mojtaba; Rezamand, Milad; Carriveau, Rupp; Ting, David S.K.; and Saif, Mehrdad. (2019). Failure Diagnosis of Wind Turbine Bearing Using Feature Extraction and a Neuro-Fuzzy Inference System (ANFIS). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11506 LNCS, 545-556.