Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequent...
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Wind power is one of the fastest-growing renewable energy sectors instrumental in the ongoing decarbonizationprocess. However, wind turbines are subjected to a wide range of dynamic loads which can cause more frequentfailures and downtime periods, leading to ever-increasing attention to effective Condition Monitoring *** this paper, we propose a novel unsupervised deep anomaly detection framework to detect anomalies in windturbines based on SCADA data. We introduce a promising neural architecture, namely a graphconvolutionalautoencoder for Multivariate Time series, to model the sensor network as a dynamical functional graph. Thisstructure improves the unsupervised learning capabilities of autoencoders by considering individual sensormeasurements together with the nonlinear correlations existing among signals. On this basis, we developeda deep anomaly detection framework that was validated on 12 failure events occurred during 20 months ofoperation of four wind turbines. The results show that the proposed framework successfully detects anomaliesand anticipates SCADA alarms by outperforming other two recent neural approaches.
Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. T...
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Unexpected faults in rotating machinery can lead to cascading disruptions of the entire work process, emphasizing the importance of early detection of performance degradation and identification of the current state. To accurately assess the health of a machine, this study introduces an FFT-based raw vibration data preprocessing and graph representation technique, which analyses changes in frequency bands to detect early degradation trends in vibration data that may appear normal. The approach proposes a methodology that utilizes a graph convolutional autoencoder trained using only normal data to extract health indicators using the differences in the vectors as degradation progresses. This approach has the advantage of using only normal data to detect subtle performance degradation early and effectively represent health indicators accordingly.
作者：
Chen, HongruixuanYokoya, NaotoWu, ChenDu, BoUniv Tokyo
Grad Sch Frontier Sci Chiba 2778561 Japan RIKEN
RIKEN Ctr Adv Intelligence Project AIP Geoinformat Unit Tokyo 1030027 Japan Wuhan Univ
State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430072 Peoples R China Wuhan Univ
Sch Comp Sci Wuhan 430072 Peoples R China Wuhan Univ
Collaborat Innovat Ctr Geospatial Technol Wuhan 430072 Peoples R China
Unsupervised multimodal change detection is a practical and challenging topic that can play an important role in time-sensitive emergency applications. To address the challenge that multimodal remote sensing images ca...
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Unsupervised multimodal change detection is a practical and challenging topic that can play an important role in time-sensitive emergency applications. To address the challenge that multimodal remote sensing images cannot be directly compared due to their modal heterogeneity, we take advantage of two types of modality-independent structural relationships in multimodal images. In particular, we present a structural relationship graph representation learning framework for measuring the similarity of the two structural relationships. First, structural graphs are generated from preprocessed multimodal image pairs by means of an object-based image analysis approach. Then, a structural relationship graph convolutional autoencoder (SR-GCAE) is proposed to learn robust and representative features from graphs. Two loss functions aiming at reconstructing vertex information and edge information are presented to make the learned representations applicable for structural relationship similarity measurement. Subsequently, the similarity levels of two structural relationships are calculated from learned graph representations, and two difference images are generated based on the similarity levels. After obtaining the difference images, an adaptive fusion strategy is presented to fuse the two difference images. Finally, a morphological filtering-based postprocessing approach is employed to refine the detection results. Experimental results on six datasets with different modal combinations demonstrate the effectiveness of the proposed method.
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