Neural network and its application on machinery fault diagnosis

The authors propose a multilayer-feedforward-network-based machine state identification method, and represent certain fuzzy relationships between the fault symptoms and causes with high nonlinearity between the input and the output of the network. As a practical diagnosis example, the rolling bearing diagnosis problem has been studied. By collecting the vibration signals of its operation and using the diagnosis model, one can make a decision about the fault causes and fault degree. Simulation experiments have shown that the proposed diagnosis method achieves better performance consisting in high correct classification rate and good flexibility.<<ETX>>