Research on the clinical classification of Xi clan diabetic foot using fuzzy C-means clustering method

The rolling bearings are considered as the heart of rotating machinery and early fault diagnosis is one of the biggest challenges during operation. Due to complicated mechanical assemblies, detection of the advancing fault and faults at the incipient stage is very difficult and tedious. This work presents a fuzzy rule based classification of bearing faults using Fuzzy C-means clustering method using vibration measurements. Experiments were conducted to collect the vibration signals of a normal bearing and bearings with faults in the inner race, outer race and ball fault. Discrete Wavelet Transform (DWT) technique is used to decompose the vibration signals into different frequency bands. In order to detect the early faults in the bearings, various statistical features were extracted from this decomposed signal of each frequency band. Based on the extracted features, Fuzzy C-means clustering method (FCM) is developed to classify the faults using suitable membership functions and fuzzy rule base is developed for each class of the bearing fault using labeled data. The experimental results show that the proposed method is able to classify the condition of the bearing using the extracted features. The proposed FCM based clustering and classification model provides easier interpretation and implementation for monitoring the condition of the rolling bearings at an early stage and it will be helpful to take the preventive action before a large-scale failure.

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A Fuzzy C-Means Clustering Method Based for 3D Mechanical Models Retrieval

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.201-203.147 ◽

2011 ◽

Vol 201-203 ◽

pp. 147-150

Author(s):

Hui Jing ◽

Cong Li ◽

Bing Kuang ◽

Mei Fa Huang ◽

Fu Yun Liu

Keyword(s):

Clustering Method ◽

Shape Distribution ◽

Fuzzy C Means ◽

Mechanical Models ◽

Fuzzy C Means Clustering ◽

Model Database ◽

The Given ◽

Query Model

To accelerate the development of new product, one important method is to use the information of the existed products. In this paper, Fuzzy C-Means clustering method (FCM) is proposed to retrieve the desired model in the given model database. In the retrieval process, first, the shape distribution histograms of the model are established and transformed into the proper format of FCM. Then, the shape distribution histograms of the model are served as inputs of FCM and classified into several groups by this algorithm. Last, all the models that belong to the classification of the query model are returned as the retrieval results. To validate the proposed method, a case study is presented. The results show that the result of proposed method is better than that of Shape Distribution. Thus, this method is suitable for 3D mechanical models retrieval.

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Application of Fuzzy C-Means Clustering Method Using Matlab To Map the Potential of Rice Plant In Bekasi Regency

SIMADA (Jurnal Sistem Informasi & Manajemen Basis Data) ◽

10.30873/simada.v1i2.1134 ◽

2018 ◽

Vol 1 (2) ◽

Author(s):

Winarni Suwarso

Keyword(s):

Data Mining ◽

Rice Plant ◽

Food Crops ◽

Clustering Method ◽

Fuzzy C Means ◽

Fuzzy C Means Clustering ◽

Rice Yields ◽

Clustering Data ◽

Fuzzy C Means Algorithm

Abstract Based on the data of rice crops from BPS-Statistics of Bekasi Regency in the field of Food Crops, there are several sub-districts in Bekasi Regency with varying rice yields. Therefore, it is necessary to group the sub-districts with the highest potential of rice producers. Therefore, a method is needed to facilitate the classification of paddy producing districts. By Fuzzy C-Means clustering method, the division of rice-producing sub-districts can be done based on the area of rice harvest (Ha) and rice production (ton). In this research, clustering of potential sub-districts using the Fuzzy C-Means algorithm is aimed at facilitating the grouping of a sub-district with the largest and low rice yields. The result is an illustration that shows the subdistrict grouping based on the results of paddy farming. Keywords: Clustering, Data Mining, Fuzzy C-Means Algorithm

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