scholarly journals A Fault Diagnosis Method Based on Semi - Supervised Fuzzy C - Means Cluster Analysis

2015 ◽  
Vol 4 (2) ◽  
pp. 281-289 ◽  
Author(s):  
Su-Qun Cao ◽  
Xinggang Ma ◽  
Youfu Zhang ◽  
Limin Luo ◽  
Fupeng Yi
Keyword(s):  
Cluster Analysis ◽  
Fault Diagnosis ◽  
Fuzzy C Means ◽  
Diagnosis Method

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.

A Bearing Intelligent Fault Diagnosis Method Based on Cluster Analysis

2012 ◽  
Vol 152-154 ◽  
pp. 1628-1633 ◽  
Author(s):  
Su Qun Cao ◽  
Xiao Ming Zuo ◽  
Ai Xiang Tao ◽  
Jun Min Wang ◽  
Xiang Zhi Chen
Keyword(s):  
Machine Learning ◽  
Cluster Analysis ◽  
Fault Diagnosis ◽  
Test Data ◽  
Rolling Bearing ◽  
Intelligent Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Fault Type ◽  
Diagnosis Method

In recent years, machine learning techniques have been widely used in intelligent fault diagnosis field. As a major unsupervised learning technology, cluster analysis plays an important role in fault intelligent diagnosis based on machine learning. In rolling bearing fault diagnosis, the traditional spectrum analysis method usually adopts the resonant demodulation technology, but when the inner circle, rolling body or multi-point faults produce composite modulation, it is difficulty to identify the fault type from demodulation spectral lines. According to this, a novel rolling bearing fault diagnosis method based on KFCM (Kernel-based Fuzzy C-Means) cluster analysis is proposed. Through clustering on test data and the known samples, the memberships of test data are obtained. From these, the rolling bearing fault type can be determined. Experimental results show that this method is effective.

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.

The Fault Diagnosis Method of Support Vector Machine Based on Kernel Fuzzy C-Means Membership

2014 ◽  
Vol 1070-1072 ◽  
pp. 2083-2086
Author(s):  
Jin Chu Zheng ◽  
Yong Qin
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Training Sample ◽  
Practical Significance ◽  
Support Vector ◽  
Nuclear Space ◽  
Fuzzy C Means ◽  
Training Sample Size ◽  
Diagnosis Method

The rolling bearing is widely used in rail vehicles. The detection and diagnosis of the bearing is of great theoretical value and practical significance. The fault diagnosis method of support vector machine (SVM) based on kernel fuzzy C-means (KFCM) membership degrees can have very good results. Support vector machine based on statistical learning theory do well in terms of classification .This method clusters according to the sample membership degrees and puts data into nuclear space. It can highlight samples of differences in features. The training sample size has been greatly reduced, while training speed and the rate of correct classification is improved. The experiment results show that this method is feasible.

scholarly journals Two-stage turnout fault diagnosis based on similarity function and fuzzy c-means

2018 ◽  
Vol 10 (12) ◽  
pp. 168781401881140 ◽  
Author(s):  
Shize Huang ◽  
Xiaolu Yang ◽  
Ling Wang ◽  
Wei Chen ◽  
Fan Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Fault Classification ◽  
Similarity Function ◽  
Reference Curve ◽  
Two Stage ◽  
Fuzzy C Means ◽  
Fault Type ◽  
Diagnosis Method ◽  
Reference Curves ◽  
Fuzzy C Means Algorithm

Fault diagnosis for turnouts is crucial to the safety of railways. Existing studies on fault diagnosis depend on human experiences to select reference curves and require fault type information beforehand. Therefore, we proposed a turnout fault diagnosis method, named similarity function and fuzzy c-means based two-stage algorithm to detect faults and identify fault types in real time. First, the reference curve is selected from current curves representing turnout actions by K-means algorithm; then, a similarity function called Fréchet distance is used to distinguish normal and abnormal curves. Second, an improved fuzzy c-means algorithm is employed to cluster curves automatically. To be more specific, it can double-confirm the normal curves recognized in the first step as well as divide the abnormal curves into different types. Furthermore, possible causes for each fault type are inferred according to their curves. Our approach integrates fault detection and fault classification into one model and would better help the diagnosis of turnouts. The analysis results based on the similarity function and fuzzy c-means based two-stage algorithm algorithm indicate that the analyzed turnout fault types can be diagnosed automatically with high accuracy. Furthermore, since the proposed similarity function and fuzzy c-means algorithm does not need to know fault types in advance, it is applicable in identifying new fault types.

Helicopter main reduction planetary gear fault diagnosis method based on SVDD

2020 ◽  
Vol 64 (1-4) ◽  
pp. 137-145
Author(s):  
Yubin Xia ◽  
Dakai Liang ◽  
Guo Zheng ◽  
Jingling Wang ◽  
Jie Zeng
Keyword(s):  
Fault Diagnosis ◽  
Planetary Gear ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Vector Data ◽  
Energy Characteristics ◽  
Gear Fault ◽  
Channel Information ◽  
Diagnosis Method

Aiming at the irregularity of the fault characteristics of the helicopter main reducer planetary gear, a fault diagnosis method based on support vector data description (SVDD) is proposed. The working condition of the helicopter is complex and changeable, and the fault characteristics of the planetary gear also show irregularity with the change of working conditions. It is impossible to diagnose the fault by the regularity of a single fault feature; so a method of SVDD based on Gaussian kernel function is used. By connecting the energy characteristics and fault characteristics of the helicopter main reducer running state signal and performing vector quantization, the planetary gear of the helicopter main reducer is characterized, and simultaneously couple the multi-channel information, which can accurately characterize the operational state of the planetary gear’s state.

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