Stray Flux Monitoring and Multi-Sensor Fusion Condition Monitoring for Squirrel Cage Induction Machines
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Luo, Genyi
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Abstract
This research work investigates the ability of external magnetic flux-based condition monitoring to detect rotor-related faults and incipient stage bearing faults in squirrel-cage induction machines (SCIMs). This work also discusses the multisensory synergy of the external magnetic flux measurement with other measurements. To investigate the stray flux-based monitoring technique, this dissertation presents a theoretical analysis of the characteristic components in the stray flux spectrum of SCIMs as well as experimental validations. A wavelet packet decomposition (WPD) denoising method is proposed for flux-based incipient bearing fault detection.
Additionally, a sensor fusion method to efficiently utilize the information from heterogeneous sensor measurements (external magnetic flux and stator current) to achieve higher rotor-related fault detection sensitivity and a higher fault type recognition rate is presented. Instead of using all the characteristic components directly, the proposed fusion method groups the features of several rotor abnormalities and then draws a conclusion on machine health status based on the abnormalities that are present in the machine.
Finally, a novel sensor fusion-based rotor vibration observer method is proposed for incipient bearing fault detection. The observer can reject the electrical disturbances from the supply side. Meanwhile, the proposed observer is less affected by the mechanical noise from lousy environment than using vibration-based monitoring.
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Date
2022-12-07
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Dissertation