Fault Diagnosis Method Based on Kernel Fuzzy C-Means Clustering with Gravitational Search Algorithm

2018 ◽  
Author(s):  
Biyuan Wu ◽  
Xiangshun Li
Keyword(s):  
Fault Diagnosis ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Diagnosis Method ◽  
Gravitational Search

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.

scholarly journals Fault Diagnosis of Bearing by Utilizing LWT-SPSR-SVD-Based RVM with Binary Gravitational Search Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Sheng-wei Fei
Keyword(s):  
Fault Diagnosis ◽  
Phase Space ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Vibration Signal ◽  
Phase Space Reconstruction ◽  
Experimental Result ◽  
Bearing Vibration ◽  
Lifting Wavelet ◽  
Gravitational Search

The fault diagnosis method of bearing based on lifting wavelet transform (LWT)-self-adaptive phase space reconstruction (SPSR)-singular value decomposition (SVD)-based relevance vector machine (RVM) with binary gravitational search algorithm (BGSA) is presented in this study, among which LWT-SPSR-SVD (LSS) is presented for feature extraction of the bearing vibration signal, the dynamic characteristics of lifting wavelet coefficients' (LWCs') reconstructed signals of the bearing vibration signal can be reflected by SPSR for LWCs' reconstructed signals of the bearing vibration signal, and BGSA is used to select the embedding space dimension and time delay of phase space reconstruction (PSR) and kernel parameter of RVM. In order to show the superiority of LWT-SPSR-SVD-based RVM with BGSA (LSS-BGSA-RVM), the traditional RVM trained by the training samples with the features based on LWT-SVD (LS-RVM) is used to compare with the proposed LSS-BGSA-RVM method. The experimental result demonstrates that compared with LS-RVM, LSS-BGSA-RVM can achieve the higher diagnosis accuracy for bearing.

Hybrid image segmentation using fuzzy c-means and gravitational search algorithm

2012 ◽  
Author(s):  
Emadaldin Mozafari Majd ◽  
M. A. As'ari ◽  
U. U. Sheikh ◽  
S. A. R. Abu-Bakar
Keyword(s):  
Image Segmentation ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Fuzzy C Means ◽  
Gravitational Search

scholarly journals Incipient Chiller Fault Diagnosis Using an Optimized Least Squares Support Vector Machine With Gravitational Search Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Yudong Xia ◽  
Ju Zhao ◽  
Qiang Ding ◽  
Aipeng Jiang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Least Squares ◽  
Principle Component Analysis ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Support Vector ◽  
Principle Component ◽  
Centrifugal Chillers ◽  
Gravitational Search

Operational faults in centrifugal chillers will lead to high energy consumption, poor indoor thermal comfort, and low operational safety, and thus it is of significance to detect and diagnose the anomalies timely and effectively, especially for those at their incipient stages. The least squares support vector machine (LSSVM) has been regarded as an effective algorithm for multiclass classification. One of the most difficult issues in LSSVM is parameter tuning. Therefore, this paper reports a development of a gravitational search algorithm (GSA) optimized LSSVM method for incipient fault diagnosis in centrifugal chillers. Considering the inadequacies of conventional principle component analysis (PCA) algorithm for nonlinear data transformation, kernel principle component analysis (KPCA) was firstly employed to reduce the dimensionality of the original input data. Secondly, an optimized “one against one” multi-class LSSVM classifier was developed and its penalty constant and kernel bandwidth were tuned by GSA. Based on the fault samples of seven typical faults at their incipient stages in chillers from ASHRAE RP 1043, the proposed GSA optimized LSSVM fault diagnostic model was trained and validated. For the purpose of demonstrating the priority of the proposed fault diagnosis method, the obtained results were compared to that of using the LSSVM classifier optimized by another two algorithms, namely, the conventional cross-validation method and particle swarm optimizer. Results showed that the best fault diagnosis performance could be achieved using the proposed GSA-LSSVM classifier. The overall average fault diagnosis accuracy for the least severity faults was reported over 95%.

scholarly journals Clustering of Huge Data with Fuzzy C-Means and applying Gravitational Search Algorithm for Optimization

2020 ◽  
Vol 8 (5) ◽  
pp. 3206-3209
Keyword(s):  
Search Algorithm ◽  
Optimization Technique ◽  
Gravitational Search Algorithm ◽  
Structure System ◽  
Clustering Technique ◽  
Fuzzy C Means ◽  
Huge Data ◽  
Bulk Data ◽  
Fcm Clustering ◽  
Gravitational Search

There is a lot of bulk data which can be efficiently structured using some Clustering mechanism, among these mechanisms Fuzzy C-Means (FCM) Clustering technique is very new and can handle this bulk data logically and in a well precise mode. FCM is a better technique when compared to K-Means as FCM is designed with Fuzzy Concerns. But clustering only cannot give precise outcome, that’s the reason we are involving an Optimization technique for tuning the results and Gravitational Search Algorithm (GSA) Optimization can makes the outcome more precise. GSA is concerned with gravity principles. GSA tailors the defects and transitions into a well structure system and finally FCM will be optimized using GSA. This System is developed with Map-Reduced method. Here in this paper, a discussion is being presented with different existing techniques that were previously used to structure the data and it is discussed how FCM with GSA is better technique when compared to those techniques and some sample Preprocessing Patterns and k-means clustering results are obtained as a first step of research.

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