Research on Fault Diagnosis of Rotating Machinery Based on Quantum Neural Network

An information fusion fault diagnosis algorithm based on the quantum neural networks is presented for the pattern recognition with overlapping classes, and it is used in the fault diagnosis of rotating machinery. By measuring the speed and acceleration of the vibration, the membership function assignment of two sensors to all fault patterns is calculated, and the fusion membership function assignment is gained by using the 5-level transfer function quantum neural networks(QNN), then according to the fusion data, the fault pattern is found. Comparing the diagnosis results based on separate original data with the ones based on QNN fused data, it is shown that the quantum fusion fault diagnosis method is more accurate.