Graph Partition Based on Dimensionless Similarity and Its Application to Fault Diagnosis

In order to improve the efficiency of fault diagnosis, a novel granular computing algorithm is developed to reduce computational cost. It is realized by extracting and partitioning on the complete graphs, and in the process of graph generation, the dimensionless similarity method is proposed to over...

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Bibliographic Details
Published inIEEE access Vol. 9; pp. 35573 - 35583
Main Authors Zheng, Bo, Gao, Huiying, Ma, Xin, Zhang, Xiaoqiang
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Computational efficiency
data mining
dimensionless similarity
Euclidean distance
Fault diagnosis
Frequency distribution
Granular computing
graph partition
Histograms
Monitoring
Partitioning algorithms
Partitions
Similarity
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Summary:In order to improve the efficiency of fault diagnosis, a novel granular computing algorithm is developed to reduce computational cost. It is realized by extracting and partitioning on the complete graphs, and in the process of graph generation, the dimensionless similarity method is proposed to overcome the influence of attributes with different dimensions. So, the algorithm is named graph partition based on dimensionless similarity (GPDS). Moreover, similarity threshold determination method based on frequency distribution histogram is proposed to reduce the dependency on the experiences of experts. Finally, different characteristic data are applied to verify the theories. The experimental results indicate that the compressed training samples can maintain the classification accuracy. Furthermore, the elapsed time can be obviously reduced. Therefore, the GPDS method can be used in fault diagnosis properly.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3059757