A new RBF neural network with GA-based fuzzy C-means clustering algorithm for SINS fault diagnosis

2009 ◽  
Author(s):  
Zhide Liu ◽  
Jiabin Chen ◽  
Chunlei Song
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Rbf Neural Network ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering

scholarly journals Axial Piston Pump Fault Diagnosis Method Based on Symmetrical Polar Coordinate Image and Fuzzy C-Means Clustering Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Wan-lu Jiang ◽  
Pei-yao Zhang ◽  
Man Li ◽  
Shu-qing Zhang
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Piston Pump ◽  
Axial Piston Pump ◽  
Fuzzy C Means ◽  
Multiple Eigenvalues ◽  
Polar Coordinate ◽  
Fuzzy C Means Clustering ◽  
Diagnosis Method ◽  
Axial Piston

In this paper, a fault diagnosis method based on symmetric polar coordinate image and Fuzzy C-Means clustering algorithm is proposed to solve the problem that the fault signal of axial piston pump is not intuitive under the time-domain waveform diagram. In this paper, the sampled vibration signals of axial piston pump were denoised firstly by the combination of ensemble empirical mode decomposition and Pearson correlation coefficient. Secondly, the data, after noise reduction, was converted into images, called snowflake images, according to symmetric polar coordinate method. Different fault types of axial piston pump can be identified by observing the snowflake images. After that, in order to evaluate the research results objectively, the obtained images were converted into Gray-Level Cooccurrence Matrixes. Their multiple eigenvalues were extracted, and the eigenvectors consisting of multiple eigenvalues were classified by Fuzzy C-Means clustering algorithm. Finally, according to the accuracy of classification results, the feasibility of applying the symmetric polar coordinate method to axial piston pump fault diagnosis has been validated.

Research on Transformer Fault Diagnosis Method Based on Artificial Immune Network and Fuzzy C-Means Clustering Algorithm

2014 ◽  
Vol 574 ◽  
pp. 468-473 ◽  
Author(s):  
Fu Zhong Wang ◽  
Shu Min Shao ◽  
Peng Fei Dong
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Power Transformer ◽  
Immune Network ◽  
Artificial Immune ◽  
Fuzzy C Means ◽  
Artificial Immune Network ◽  
Fuzzy C Means Clustering ◽  
Diagnosis Method ◽  
Transformer Fault

The transformer is one of the indispensable equipment in transformer substation, it is of great significance for fault diagnosis. In order to accurately judge the transformer fault types, an algorithm is proposed based on artificial immune network combined with fuzzy c-means clustering to study on transformer fault samples. Focus on the introduction of data processing of transformer faults based on artificial immune network, the identification of transformer faults based on fuzzy c-means clustering, and the simulation process. The experimental results show that the proposed algorithm can classify power transformer fault types effectively, and the algorithm has a good application prospect in the transformer fault diagnosis.

Fault diagnosis of hydraulic piston pumps based on a two-step EMD method and fuzzy C-means clustering

2016 ◽  
Vol 230 (16) ◽  
pp. 2913-2928 ◽  
Author(s):  
Chuanqi Lu ◽  
Shaoping Wang ◽  
Chao Zhang
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Periodic Signal ◽  
Hydraulic Systems ◽  
End Effects ◽  
Characteristic Energy ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Discharge Pressure ◽  
Emd Method

Hydraulic piston pumps are commonly used in aircrafts and various other equipment, and efficient fault diagnosis of them is playing an important role in improving the reliability and performance of hydraulic systems. Given that the discharge pressure signal of piston pump is a quasi-periodic signal and contains variety of state information, this article proposes a fault diagnosis method combining a two-step empirical mode decomposition (EMD) method based on waveform matching and extrema mirror extension with fuzzy C-means clustering. Based upon discharge pressure signals of piston pumps, the two-step EMD method which can restrain the end effects of traditional EMD is adopted to decompose the original signal. Characteristic vectors are then constructed by computing the normalized characteristic energy of selected Intrinsic Mode Function (IMF) components on the basis of local Hilbert marginal energy spectrum. Finally, fuzzy C-means clustering algorithm is used to identify the faults of pumps. Experimental results indicate that the proposed method can identify the faults of pumps effectively.

scholarly journals Neural Network Based Fuzzy C-MEANS Clustering Algorithm

2011 ◽  
pp. 100-104
Author(s):  
Suneetha Chittinen ◽  
Dr. Raveendra Babu Bhogapathi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Sets ◽  
Fuzzy C Means ◽  
Fast Learning ◽  
Presentation Sequence ◽  
Fuzzy C Means Clustering ◽  
Artificial Neural

In this paper, fuzzy c-means algorithm uses neural network algorithm is presented. In pattern recognition, fuzzy clustering algorithms have demonstrated advantage over crisp clustering algorithms to group the high dimensional data into clusters. The proposed work involves two steps. First, a recently developed and Enhanced Kmeans Fast Leaning Artificial Neural Network (KFLANN) frame work is used to determine cluster centers. Secondly, Fuzzy C-means uses these cluster centers to generate fuzzy membership functions. Enhanced K-means Fast Learning Artificial Neural Network (KFLANN) is an algorithm which produces consistent classification of the vectors in to the same clusters regardless of the data presentation sequence. Experiments are conducted on two artificial data sets Iris and New Thyroid. The result shows that Enhanced KFLANN is faster to generate consistent cluster centers and utilizes these for elicitation of efficient fuzzy memberships.

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