Rough sets and partially-linearized neural network for structural fault diagnosis of rotating machinery

Structural faults, such as unbalance, misalignment and looseness etc., are often occurring in a shaft of rotating machinery. These faults may cause serious machine accidents and bring great production losses. In order to detect faults and distinguish fault type at an early stage, this paper proposes a diagnosis method by using "Partially-linearized Neural Network (PNN)" by which the type of structural faults can be automatically distinguished on the basis of the probability distributions of symptom parameters. The symptom parameters are non-dimensional parameters which reflect the characteristics of time signal measured for diagnosis of rotating machinery. The knowledge for the PNN learning can be acquired by using the Rough Sets of the symptom parameters. The practical examples of diagnosis for rotating machinery are shown to verify the efficiency of the method.