Fault Diagnosis Based on Particle Swarm Fuzzy Clustering Algorithm

2011 ◽  
Vol 63-64 ◽  
pp. 111-114
Author(s):  
Zhi Xiao Wang ◽  
Qiang Niu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Data Set ◽  
Fuzzy Clustering Algorithm ◽  
Efficient Performance ◽  
New Strategy ◽  
Fuzzy C Means Clustering ◽  
Search Capability

Fuzzy c-means clustering algorithm (FCM) is sensitive to noise and less effective when handling high dimensional data set. Given that particle swarm optimization algorithm (PSO) has strong global search capability and efficient performance, a new PSO based fuzzy clustering algorithm is proposed. Particles in the new algorithm are encoded by membership in FCM. The new algorithm adopts a new strategy to meet the constraints of FCM, so as to optimize the clustering effect of FCM. Finally, this algorithm is applied to motor fault diagnosis. Experiment shows that the new algorithm made up for the shortcomings of FCM, improved the efficiency and accuracy of clustering and bettered fault diagnosis results.

An approach for fault diagnosis using a novel hybrid fuzzy clustering algorithm

2018 ◽  
Author(s):  
Adrian Rodriguez Ramos ◽  
Carlos Cruz Corona ◽  
Jose Luis Verdegay ◽  
Antonio Jose da Silva Neto ◽  
Orestes Llanes-Santiago
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Fuzzy Clustering Algorithm

scholarly journals Fuzzy Clustering Algorithm with Non-Neighborhood Spatial Information for Surface Roughness Measurement Based on the Reflected Aliasing Images

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3285 ◽  
Author(s):  
Hang Zhang ◽  
Jian Liu ◽  
Lin Chen ◽  
Ning Chen ◽  
Xiao Yang
Keyword(s):  
Surface Roughness ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Roughness Measurement ◽  
Surface Roughness Measurement ◽  
Fuzzy Clustering Algorithm ◽  
Fuzzy C Means Clustering ◽  
Highly Correlated

Due to the limitation of the fixed structures of neighborhood windows, the quality of spatial information obtained from the neighborhood pixels may be affected by noise. In order to compensate this drawback, a robust fuzzy c-means clustering with non-neighborhood spatial information (FCM_NNS) is presented. Through incorporating non-neighborhood spatial information, the robustness performance of the proposed FCM_NNS with respect to the noise can be significantly improved. The results indicate that FCM_NNS is very effective and robust to noisy aliasing images. Moreover, the comparison of other seven roughness indexes indicates that the proposed FCM_NNS-based F index can characterize the aliasing degree in the surface images and is highly correlated with surface roughness (R2 = 0.9327 for thirty grinding samples).

scholarly journals Service Partition Method Based on Particle Swarm Fuzzy Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hong Xia ◽  
Qingyi Dong ◽  
Hui Gao ◽  
Yanping Chen ◽  
ZhongMin Wang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Particle Swarm ◽  
Cluster Center ◽  
Fuzzy Clustering Algorithm ◽  
Partition Method ◽  
Service Data ◽  
Optimal Cluster ◽  
Better Than

It is difficult to accurately classify a service into specific service clusters for the multirelationships between services. To solve this problem, this paper proposes a service partition method based on particle swarm fuzzy clustering, which can effectively consider multirelationships between services by using a fuzzy clustering algorithm. Firstly, the algorithm for automatically determining the number of clusters is to determine the number of service clusters based on the density of the service core point. Secondly, the fuzzy c -means combined with particle swarm optimization algorithm to find the optimal cluster center of the service. Finally, the fuzzy clustering algorithm uses the improved Gram-cosine similarity to obtain the final results. Extensive experiments on real web service data show that our method is better than mainstream clustering algorithms in accuracy.

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