Finite element modeling and analysis of signal based localization of fatigue crack in active magnetic bearing supported shafts

This study addresses the intricate challenge of detecting and localizing transverse cracks in rotors supported by active magnetic bearings (AMBs). Rotor systems in machinery demand accurate fault identification without halting operations, a task accomplished through signal-based analysis. Vibration signals, reflecting the mechanical state during operation, serve as valuable information sources for condition assessment. The significance of this study lies in its dual objectives: identification and localization of rotor cracks. Unlike existing techniques that often focus solely on crack identification, this work ventures into the challenging domain of precise localization. An innovative and novel proof-by-negation method is introduced, enabling the identification algorithm to pinpoint crack positions. This methodology offers a breakthrough by efficiently addressing the intricate problem of accurate localization within the dynamic rotor system. The study's methodology involves a comprehensive approach, combining finite element modeling, synthetic vibration response generation, and advanced identification techniques. The algorithm's efficacy is demonstrated through simulations using MATLAB-based finite element models, encompassing both conventionally supported and AMB-supported rotor configurations. Synthetic responses are extracted and processed to validate the algorithm's effectiveness in identifying and localizing cracks. The results showcase the algorithm's robustness and accuracy. In AMB-supported rotor systems, the algorithm successfully estimates key parameters such as crack stiffness and position. By manipulating a flag vector, the proof-by-negation approach accurately locates the crack along the rotor's axis.