A Multi-Fault Detection Method With Improved Triplet Loss Based on Hard Sample Mining

Abstract

Fault detection plays an essential role in the power generation of wind turbines (WTs). However, most of the present fault detection methods are designed to detect a certain type of fault. When these methods are used to detect multiple fault types, their accuracies tend to decline. In this paper, an effective multi-fault detection method based on improved triplet loss is proposed. First, a hard sample mining method is presented based on WT data for the first time, so that the samples which are more suitable for triplet training are selected. Second, triplet training is improved by applying a new mapping layer and optimizing the loss function. Third, based on the improved triplet training, a deep learning model is trained to map the original samples into feature vectors in an embedding space, so that the distance between two samples in the same class is shortened whereas that of different classes is lengthened. Then, a multi-classification model is trained to distinguish the feature vectors, so that the faults of different types can be detected. Finally, four groups of experiments are conducted using real WT SCADA data collected from a wind farm in northern China. The experiment results show that the proposed method is effective.