Intelligent Diagnosis Method for Rotating Machinery Using Ant Colony Optimization

Ke Li; Peng Chen; Hao Sun

doi:10.4028/www.scientific.net/AMR.518-523.3814

Paper Titles

A Field Study Method for Soundscape in Railway Stations
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Denoising Simulation Study for MEMS Gyro Dynamic Running
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Effect of Users' Social Characteristics on Soundscape in the Waiting Halls of Railway Stations
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Evaluation of Sound Environment in the Waiting Halls of Railway Stations
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Intelligent Diagnosis Method for Rotating Machinery Using Ant Colony Optimization
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RETRACTED: Low-Order Frequency Response Model in the Application of Frequency Control
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The Analysis of Vibration Fault Features and Vibration Mechanism Caused by Rotating Machinery Loosening
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The Influence of Users' Behavioral Characteristics on Soundscape in the Waiting Halls of Railway Stations
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Analytical Sensitivity of Acoustic Power Radiated from a Closed Structure Based on the Theory of Acoustic Radiation Modes
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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 518-523Intelligent Diagnosis Method for Rotating...

Intelligent Diagnosis Method for Rotating Machinery Using Ant Colony Optimization

Abstract:

This paper proposes an intelligent method for diagnosing structural faults of rotating machinery using ant colony optimization (ACO) and non-dimensional symptom parameters (NSPs) in order to detect faults and distinguish fault types at an early stage. NSPs are defined for reflecting the features of vibration signals measured in each state. Detection index (DI) using statistical theory has also been defined to evaluate the applicability of the NSPs. The DI can be used to indicate the fitness of an NSP for ACO. Lastly, the state identification for the condition diagnosis of rotating machinery is converted to a clustering problem of the values of NSPs calculated from vibration signals in different states of the machine. Ant colony optimization (ACO) is also introduced for this purpose. Practical examples of fault diagnosis for rotating machinery are provided to verify the effectiveness of the proposed method. The verification results show that the structural faults often occurring in a rotation machinery, such as a unbalance, a misalignment and a looseness states are effectively identified by the proposed method.

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Periodical:

Advanced Materials Research (Volumes 518-523)

Pages:

3814-3819

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.518-523.3814

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Online since:

May 2012

Authors:

Ke Li, Peng Chen, Hao Sun

Keywords:

Ant Colony Optimization (ACO), Fault Diagnosis, Non-Dimensional Symptom Parameter, Structural Fault

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