Research on Application of AP Clustering Algorithm in Fault Diagnosis

2018 ◽  
Author(s):  
Juncai Lin ◽  
Qing Yang
Keyword(s):  
Fault Diagnosis ◽  
Clustering Algorithm ◽  
Research On Application ◽  
Ap Clustering

Research on Intelligent Diagnosis Technology of Transformer Fault

2013 ◽  
Vol 385-386 ◽  
pp. 589-592
Author(s):  
Hong Qi Wu ◽  
Xiao Bin Li
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Radial Basis Function ◽  
Basis Function ◽  
Rbf Neural Network ◽  
Intelligent Diagnosis ◽  
Rbf Network ◽  
Working Principle ◽  
Research On Application ◽  
Transformer Fault

In order to improve the diagnosis rates of transformer fault, a research on application of RBF neural network is carried out. The structure and working principle of radial basis function (RBF) neural network are analyzed and a three layer RBF network is also designed for transformer fault diagnosis. It is proved by MATLAB experiment that RBF neural network is a strong classifier which is used to diagnose transformer fault effectively.

scholarly journals Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

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 A Clustering-Based Automatic Transfer Function Design for Volume Visualization

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Tianjin Zhang ◽  
Zongrui Yi ◽  
Jinta Zheng ◽  
Dong C. Liu ◽  
Wai-Mai Pang ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Spatial Information ◽  
Transfer Functions ◽  
Clustering Algorithms ◽  
Volume Visualization ◽  
Automatic Generation ◽  
Volume Data ◽  
3D Volume ◽  
Ap Clustering ◽  
Different Tissues

The two-dimensional transfer functions (TFs) designed based on intensity-gradient magnitude (IGM) histogram are effective tools for the visualization and exploration of 3D volume data. However, traditional design methods usually depend on multiple times of trial-and-error. We propose a novel method for the automatic generation of transfer functions by performing the affinity propagation (AP) clustering algorithm on the IGM histogram. Compared with previous clustering algorithms that were employed in volume visualization, the AP clustering algorithm has much faster convergence speed and can achieve more accurate clustering results. In order to obtain meaningful clustering results, we introduce two similarity measurements: IGM similarity and spatial similarity. These two similarity measurements can effectively bring the voxels of the same tissue together and differentiate the voxels of different tissues so that the generated TFs can assign different optical properties to different tissues. Before performing the clustering algorithm on the IGM histogram, we propose to remove noisy voxels based on the spatial information of voxels. Our method does not require users to input the number of clusters, and the classification and visualization process is automatic and efficient. Experiments on various datasets demonstrate the effectiveness of the proposed method.

scholarly journals K-Nearest Neighbor Intervals Based AP Clustering Algorithm for Large Incomplete Data

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cheng Lu ◽  
Shiji Song ◽  
Cheng Wu
Keyword(s):  
Clustering Analysis ◽  
Incomplete Data ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Interval Data ◽  
Similarity Function ◽  
K Nearest Neighbor ◽  
Partial Data ◽  
Missing Attributes ◽  
Ap Clustering

The Affinity Propagation (AP) algorithm is an effective algorithm for clustering analysis, but it can not be directly applicable to the case of incomplete data. In view of the prevalence of missing data and the uncertainty of missing attributes, we put forward a modified AP clustering algorithm based onK-nearest neighbor intervals (KNNI) for incomplete data. Based on an Improved Partial Data Strategy, the proposed algorithm estimates the KNNI representation of missing attributes by using the attribute distribution information of the available data. The similarity function can be changed by dealing with the interval data. Then the improved AP algorithm can be applicable to the case of incomplete data. Experiments on several UCI datasets show that the proposed algorithm achieves impressive clustering results.

scholarly journals Class Imbalanced Fault Diagnosis via Combining K-Means Clustering Algorithm with Generative Adversarial Networks

2021 ◽  
Vol 25 (3) ◽  
pp. 346-355
Author(s):  
Huifang Li ◽  
◽  
Rui Fan ◽  
Qisong Shi ◽  
Zijian Du
Keyword(s):  
Machine Learning ◽  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Clustering Algorithm ◽  
Generative Adversarial Networks ◽  
Similar Distribution ◽  
Generative Adversarial Network ◽  
Minority Class ◽  
Adversarial Network ◽  
Original Dataset

Recent advancements in machine learning and communication technologies have enabled new approaches to automated fault diagnosis and detection in industrial systems. Given wide variation in occurrence frequencies of different classes of faults, the class distribution of real-world industrial fault data is usually imbalanced. However, most prior machine learning-based classification methods do not take this imbalance into consideration, and thus tend to be biased toward recognizing the majority classes and result in poor accuracy for minority ones. To solve such problems, we propose a k-means clustering generative adversarial network (KM-GAN)-based fault diagnosis approach able to reduce imbalance in fault data and improve diagnostic accuracy for minority classes. First, we design a new k-means clustering algorithm and GAN-based oversampling method to generate diverse minority-class samples obeying the similar distribution to the original minority data. The k-means clustering algorithm is adopted to divide minority-class samples into k clusters, while a GAN is applied to learn the data distribution of the resulting clusters and generate a given number of minority-class samples as a supplement to the original dataset. Then, we construct a deep neural network (DNN) and deep belief network (DBN)-based heterogeneous ensemble model as a fault classifier to improve generalization, in which DNN and DBN models are trained separately on the resulting dataset, and then the outputs from both are averaged as the final diagnostic result. A series of comparative experiments are conducted to verify the effectiveness of our proposed method, and the experimental results show that our method can improve diagnostic accuracy for minority-class samples.

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.

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