Rotor fault diagnosis system based on sGA-based individual neural networks

This paper proposes a robust fault diagnosis system of rotating machine adapting machine learning technology. The kernel of this diagnosis system includes a set of individual neural networks based on structured genetic algorithm (sGAINNs). First, the frequency characteristics from differential signals, including fast Fourier transform (FFT) and full spectrum, are used to feed into the sGAINNs corresponding to assigned faults to emphasize the phenomenon of each fault. Especially, the structured genetic algorithm is applied to get the optimal parameters of the above sGAINNs. In the final step of proposed diagnosis system, the evaluated indexes from sGAINNs are synthesized by a reasoning engine to identify the faults in the rotor system. Finally, six common faults of rotor system, unbalance, bow, misalignment, rub, whirl, and whip, are generated from a rotor kit, produced by Bently Nevada Corporation, to verify the performance of this diagnosis system. The advantage of this diagnosis system is that the optimal sGAINNs parameters can be automatically obtained, the local optimal solutions can be reduced and the diagnosis accuracy can be improved.