Precisely identifying internal excitation parameters within rotor-bearing systems is imperative for ensuring reliability and optimal performance. This paper meticulously addresses various issues, such as coupling misalignment, residual bow, internal damping, and imbalance. However, the novelty of the work lies in its detailed exploration of the interplay between these parameters, particularly emphasizing the multiplicative effect resulting from residual bow and misalignment. Introducing an innovative approach, the paper employs an active magnetic bearing (AMB) near the output rotor's disc to mitigate vibrations and enhance system stability. A robust identification algorithm is developed, leveraging least-squares fitting in the frequency domain to accurately estimate multiple internal excitation parameters and system characteristics. These parameters encompass viscous damping, internal damping, unbalance, residual bow, static and dynamic coupling misalignment, multiplicative force, and various AMB constants. The algorithm's resilience is demonstrated through rigorous testing against noise percentage errors. Furthermore, practical experimentation using a laboratory test rig validates its efficacy. Response data from proximity probes are inputted into the algorithm, successfully identifying parameters. Experimental validation is achieved by comparing irregularities in orbit plots and full spectra with numerical simulations based on the identified parameters, affirming the algorithm's reliability in real-world scenarios. This comprehensive approach offers a dependable solution for internal excitation identification in complex rotor systems.
