Intelligent Diagnosis Method for Rotating Machinery Using Ant Colony Optimization

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.