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Department of Environmental Science and Engineering, Faculty of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu-shi, Mie-ken, 514-8507, Japan





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Abstract Structural faults, such as unbalance, misalignment and looseness, etc., often occur in the shafts of rotating machinery. These faults may cause serious machine accidents and lead to great production losses. This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization ACO and relative ratio symptom parameters RRSPs in order to detect faults and distinguish fault types at an early stage. New symptom parameters called -relative ratio symptom parameters- are defined for reflecting the features of vibration signals measured in each state. Synthetic detection index SDI using statistical theory has also been defined to evaluate the applicability of the RRSPs. The SDI can be used to indicate the fitness of a RRSP for ACO. Lastly, this paper also compares the proposed method with the conventional neural networks NN method. Practical examples of fault diagnosis for a centrifugal fan are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in the centrifugal fan, such as unbalance, misalignment and looseness states are effectively identified by the proposed method, while these faults are difficult to detect using conventional neural networks. View Full-Text

Keywords: rotating machinery; structural fault; relative ratio symptom parameter; ant colony optimization rotating machinery; structural fault; relative ratio symptom parameter; ant colony optimization





Author: Ke Li and Peng Chen *

Source: http://mdpi.com/



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