Design of rough-neural network fault diagnosis system for turntable

In view of the diversity and complexity of the turntable failure, an expert-independent fault diagnosis system was designed based on rough-neural network. Firstly, the fault diagnosis decision table was established, and then the attributes are reduced by a rough-set method. Finally, the neural network classifier and recognizer were designed. The experiment results show that the diagnosis system could distinguish and identify the different faults with the same failure phenomena, and the diagnostic accuracy is up to 96.7%. By combing the rough sets with neural network, the rough sets can reduce the attributes and delete the redundancy. The rough-neural network can simplify the training sets, reduce the complexity of the neural network structure, and has the powerful fault tolerance and anti-jamming capability. The system has strong engineering practicality.