Randomized trial demonstrates dynamic model accuracy for misalignment identification in rotating systems, indicating improved fault detection methods.
Significant axial vibration, loopy orbits, and multiple harmonics with a principal 2X component are the primary methods used to identify misalignment in the rotating systems. In this study, a novel method for the modeling and identification of misalignments in a coupled shaft-disk system represented by two nodes of Timoshenko beam finite elements is developed. A typical coupled turbogenerator system comprises two individual rotors interconnected using a coupling and held up by two squeeze film dampers (SFDs). An interaction between both the generator and the turbine rotor systems is modelled mathematically using a coupling model that includes a stiffness matrix with two elements: perpetual coupling stiffness (PCS) and time-dependent coupling stiffness (TCS). The effect of misalignment forcing characteristics is simulated with a suitable direct forcing function for full spectrum FFT. Active magnetic bearings (AMBs) are auxiliary bearings used in each rotor for vibration reduction and fault detection. The development of the dynamic model is carried further through an identification procedure built using the least-squares fitting approach of inverse analysis. This paper represents the intensity of TCS magnitude in the determination of system angular misalignment. AMB constants, coupling stiffness coefficients, bearing dynamic parameters, and unbalances are all determined using the least-squares linear estimation approach. Testing has been done on the identification algorithm and sensitivity to skew errors in modeling parameters and signal noise. The present study is unique in that it employs the TCS coefficient matrix, which provides precise indications of both the degree and type of misalignment.
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S et al. (2026) studied this question.
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