Automatic Diagnosis of Microgrid Networks’ Power Device Faults Based on Stacked Denoising Autoencoders and Adaptive Affinity Propagation Clustering

This paper presents a model based on stacked denoising autoencoders (SDAEs) in deep learning and adaptive affinity propagation (adAP) for bearing fault diagnosis automatically. First, SDAEs are used to extract potential fault features and directly reduce their high dimension to 3. To prove that the...

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Vydáno v:Complexity (New York, N.Y.) Ročník 2020; číslo 2020; s. 1 - 24
Hlavní autoři: Fan, Bo, Zhang, Xiaodi, Shu, Xin, Xu, Fan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 26.07.2020
Hindawi
John Wiley & Sons, Inc
Wiley
Témata:
Affinity
Algorithms
Bias
Cluster analysis
Clustering
Comparative analysis
Damping
Datasets
Distributed generation
Fault diagnosis
Feature extraction
Machine learning
Mathematical models
Methods
Noise
Noise reduction
Parameters
Principal components analysis
Propagation
Three dimensional models
China
ISSN:1076-2787, 1099-0526
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Shrnutí:This paper presents a model based on stacked denoising autoencoders (SDAEs) in deep learning and adaptive affinity propagation (adAP) for bearing fault diagnosis automatically. First, SDAEs are used to extract potential fault features and directly reduce their high dimension to 3. To prove that the feature extraction capability of SDAEs is better than stacked autoencoders (SAEs), principal component analysis (PCA) is employed to compare and reduce their dimension to 3, except for the final hidden layer. Hence, the extracted 3-dimensional features are chosen as the input for adAP cluster models. Compared with other traditional cluster methods, such as the Fuzzy C-mean (FCM), Gustafson–Kessel (GK), Gath–Geva (GG), and affinity propagation (AP), clustering algorithms can identify fault samples without cluster center number selection. However, AP needs to set two key parameters depending on manual experience—the damping factor and the bias parameter—before its calculation. To overcome this drawback, adAP is introduced in this paper. The adAP clustering algorithm can find the available parameters according to the fitness function automatic. Finally, the experimental results prove that SDAEs with adAP are better than other models, including SDAE-FCM/GK/GG according to the cluster assess index (Silhouette) and the classification error rate.
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content type line 14
ISSN:1076-2787
1099-0526
DOI:10.1155/2020/8509142