Physics-informed neural network for system identification of rotors

The condition of the rotor system remains difficult to assess due to system nonlinearity and nosiy measurements. To deal with the problem, we proposed a hierarchical physics-informed neural network (HPINN) to discover the ordinary differential equation (ODE) of a healthy/faulty rotor system from noise measurements and then assess the machine condition based on the discovered ODE. Specifically, the ODE of a healthy rotor system is first stably identified from noise measurement through HPINN guided by rotor dynamics. Based on the identified healthy ODE, the extra fault terms in the ODE of the faulty rotor system are then sparsely regressed from the predefined library embedded in HPINN. Moreover, with the mathematical terms of discovered fault, the potential fault and the health indicator (HI) are diagnosed and constructed to assess the condition of the rotor system, respectively. Finally, the effectiveness of the proposed method is verified by the data set collected on the circulating water test bench, showing the potential for practical applications.