Vibration signals of complex rotating machinery are often cyclostationary, so in this paper one novel method is proposed to detect and predict early faults based on the linear (almost) periodically time-varying autoregressive (LPTV-AR) model. At first the algorithms of identifying model parameters and order are presented using the higher-order cyclic-cumulant, which can suppress additive stationary noises and improve the signal to noise ratio (SNR). Then numerical simulations are done and the results indicate that this model is more effective for cyclostationary signals than the classical AR model. In the end the proposed method is used for detecting incipient gear crack fault in a helicopter gearbox. The results demonstrate that the approach can be used to detect and predict early faults of complex rotating machinery by the kurtosis of the residual signal.

Induction motors are complex energy conversion systems across the domains of dynamics, electricity, and magnetism. Most existing models mainly consider unidirectional coupling, such as the effect of dynamics on electromagnetic properties, or the effect of unbalanced magnetic pull on dynamics, while in practice it should be a bidirectional coupling effect. The bidirectionally coupled electromagnetic-dynamics model is beneficial to the analysis of induction motor fault mechanisms and characteristics. This paper proposes a coupled electromagnetic-dynamic modeling method that introduces unbalanced magnetic pull. By using the rotor velocity, air gap length, and unbalanced magnetic pull as the coupling parameters, the coupled simulation of the dynamic and electromagnetic models can be effectively realized. Simulation results for bearing faults show that the introduction of magnetic pull induces a more complex dynamic behavior of the rotor, which in turn leads to modulation in the vibration spectrum. The fault characteristics can be found in the frequency domain of the vibration and current signals. Through the comparison between simulation and experimental results, the effectiveness of the coupled modeling approach and the frequency domain characteristics caused by the unbalanced magnetic pull are verified. The proposed model can help to obtain a variety of information that is difficult to measure in reality and can also serve as a technical basis for further research on nonlinear characteristics and chaos in induction motors.