Paper Info
Title
WaveletFCNN: A Deep Time Series Classification Model for Wind Turbine Blade Icing Detection.
Abstract
Wind power, as an alternative to burning fossil fuels, is plentiful and renewable. Data-driven approaches are increasingly popular for inspecting the wind turbine failures. In this paper, we propose a novel classification-based anomaly detection system for icing detection of the wind turbine blades. effectively combine the deep neural networks and wavelet transformation to identify such failures sequentially across the time. In the training phase, we present a wavelet based fully convolutional neural network (FCNN), namely WaveletFCNN, for the time series classification. We improve the original (FCNN) by augmenting features with the wavelet coefficients. WaveletFCNN outperforms the state-of-the-art FCNN for the univariate time series classification on the UCR time series archive benchmarks. In the detecting phase, we combine the sliding window and majority vote algorithms to provide the timely monitoring of the anomalies. The system has been successfully implemented on a real-world dataset from Goldwind Inc, where the classifier is trained on a multivariate time series dataset and the monitoring algorithm is implemented to capture the abnormal condition on signals from a wind farm.
Year
Venue
DocType
2019
arXiv: Learning
Journal
Volume
Citations 
PageRank 
abs/1902.05625
1
0.35
References 
Authors
25
6
Name
Order
Citations
PageRank
Binhang Yuan1116.94
Chen Wang236193.70
Fei Jiang3183.62
Mingsheng Long4142155.15
Philip S. Yu5306703474.16
Yuan Liu6710.65