C-means Clustering Algorithm and Its Applications in Fault Diagnosis of Equipment
Author(s): MENG Xian-yao HAN Xin-jie MENG Song
Pages: 198-200+220
Year: 2006 Issue: S2
Journal: Control Engineering of China
Keyword: fuzzy clustering; fault diagnosis; C-means algorithm; equipment;
Abstract: The traditional fault detection method for the large equipment is not helpful because of the complicated relationship between the fault symptoms and causes of the equipment.A fuzzy C-means clustering algorithm is used and the features of faults and symptoms of the detected object are classified based on the established fuzzy connection matrix.The comparison between the fault symptom clusters collected from an equipment recently and the previous outcomes of the fault symptoms of the equipment are made.Then the closest outcomes are identified and the fault is spotted.A case of the recent fault detection for the shafting of main engine fully proves the effectiveness of the above-mentioned method.
Pages: 198-200+220
Year: 2006 Issue: S2
Journal: Control Engineering of China
Keyword: fuzzy clustering; fault diagnosis; C-means algorithm; equipment;
Abstract: The traditional fault detection method for the large equipment is not helpful because of the complicated relationship between the fault symptoms and causes of the equipment.A fuzzy C-means clustering algorithm is used and the features of faults and symptoms of the detected object are classified based on the established fuzzy connection matrix.The comparison between the fault symptom clusters collected from an equipment recently and the previous outcomes of the fault symptoms of the equipment are made.Then the closest outcomes are identified and the fault is spotted.A case of the recent fault detection for the shafting of main engine fully proves the effectiveness of the above-mentioned method.
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