Wind Turbine Gearbox Failure Detection Based on SCADA Data: A Deep Learning-Based Approach

Abstract

Gearbox failure is one of top-ranked factors leading to the unavailability of wind turbines (WTs). Existing data-driven studies of gearbox failure detection (GFD) focus on improving detection accuracies while reducing false alarms has not received sufficient discussions. In this article, we propose a deep joint variational autoencoder (JVAE)-based monitoring method using wind farm supervisory control and data acquisition (SCADA) data to more effectively detect WT gearbox failures. The JVAE-based monitoring method includes two parts. First, a novel JVAE that takes a chunk of multivariate time series derived from collected SCADA data as inputs is developed. The JVAE utilizes two types of predefined parameters, behavior parameters (BPs) and conditional parameters (CPs), to produce reconstruction errors (REs) of the BP, which reflects the gearbox abnormality. Next, a statistical process control chart is developed to monitor REs and raise alarms. To validate advantages of the proposed method in GFD, five methods, the joint latent variational autoencoder (JLVAE)-, the variational autoencoder (VAE)-, full-dimensional VAE (FDVAE)-, recurrent autoencoder (RAE)-, and one-class support vector machine (OCSVM)-based monitoring methods, are considered as benchmarks. SCADA data with field reports of gearbox failure events collected from four commercial wind farms are utilized to demonstrate the effectiveness of the JVAE-based monitoring method on GFD and its stronger ability to resist false alarms.