Machine Learning-Based Adaptive Fault Detection Method for Wind Turbine Gearboxes with Imbalanced Data through An IIoT Platform

Abstract

In the context of Industry 4.0, machine learning algorithms have been commonly used to monitor the health state of wind turbine gearboxes to avoid catastrophic failure and reduce maintenance costs. However, due to the lack of a certain category of data (i.e., healthy or faulty) and the various working conditions of wind turbines, many existing methods may not provide reliable results in practical industrial applications. To solve this problem, we create an industrial internet of things (IIoT) platform, through which a machine learning-based adaptive fault detection method for wind turbine gearboxes is proposed. The features are extracted and adapted to fine-tune the pre-trained model on newly arriving samples from different wind turbines, components, or failure modes. The adaptation performance is evaluated with accuracy, false alarm rate, and fault detection rate. Case studies are then performed using highfrequency vibration signals acquired from two megawatts (MW) onshore wind turbines. The results show that the proposed adaptive method significantly improves the fault detection performance when class distribution is not balanced, and can be easily applied to the fault diagnosis of large numbers of wind turbines. This, integrated with the IIoT platform that alleviates the shortage of computational and storage capacity in wind farms and requires less user involvement, allows for a more effective condition monitoring system.