Sensors 2011, 11(4), 4009-4029; doi:10.3390/s110404009
Article

Intelligent Method for Diagnosing Structural Faults of Rotating Machinery Using Ant Colony Optimization

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Received: 9 March 2011; in revised form: 1 April 2011 / Accepted: 6 April 2011 / Published: 6 April 2011
(This article belongs to the Special Issue 10 Years Sensors - A Decade of Publishing)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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.
Keywords: rotating machinery; structural fault; relative ratio symptom parameter; ant colony optimization
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MDPI and ACS Style

Li, K.; Chen, P. Intelligent Method for Diagnosing Structural Faults of Rotating Machinery Using Ant Colony Optimization. Sensors 2011, 11, 4009-4029.

AMA Style

Li K, Chen P. Intelligent Method for Diagnosing Structural Faults of Rotating Machinery Using Ant Colony Optimization. Sensors. 2011; 11(4):4009-4029.

Chicago/Turabian Style

Li, Ke; Chen, Peng. 2011. "Intelligent Method for Diagnosing Structural Faults of Rotating Machinery Using Ant Colony Optimization." Sensors 11, no. 4: 4009-4029.

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