Analysis Of Electrical Signatures In Synchronous Generators Characterized By Bearing Faults
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Analysis of Electrical Signatures in Synchronous Generators Characterized by Bearing Faults
Synchronous generators play a vital role in power systems. One of the major mechanical faults in synchronous generators is related to bearings. The popular vibration analysis method has been utilized to detect bearing faults for years. However, bearing health monitoring based on vibration analysis is expensive. One of the reasons is because vibration analysis requires costly vibration sensors and the extra costs associated with its proper installation and maintenance. This limitation prevents continuous bearing condition monitoring, which gives better performance for rolling element bearing fault detection, compared to the periodic monitoring method that is a typical practice for bearing maintenance in industry. Therefore, a cost effective alternative is necessary. In this study, a sensorless bearing fault detection method for synchronous generators is proposed based on the analysis of electrical signatures, and its bearing fault detection capability is demonstrated. Experiments with staged bearing faults are conducted to validate the effectiveness of the proposed fault detection method. First, a generator test bed with an in- situ bearing damage device is designed and built. Next, multiple bearing damage experiments are carried out in two vastly different operating conditions in order to obtain statistically significant results. During each experiment, artificially induced bearing current causes accelerated damage to the front bearing of the generator. This in-situ bearing damage process entirely eliminates the necessity of disassembly and reassembly of the experimental setup that causes armature spectral distortions. The electrical fault indicator is computed based on stator voltage signatures without the knowledge of machine and bearing specific parameters. Experimental results are compared using the electrical indicator and a vibration indicator that is calculated based on measured vibration data. The results indicate that the electrical indicator can be used to analyze health degradation of rolling element bearings in synchronous generators in most instances. Though the vibration indicator enables early bearing fault detection, it is found that the electrical fault indicator is also capable of detecting bearing faults well before catastrophic bearing failure.
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Condition Monitoring and Faults Diagnosis of Induction Motors
The book covers various issues related to machinery condition monitoring, signal processing and conditioning, instrumentation and measurements, faults for induction motors failures, new trends in condition monitoring, and the fault identification process using motor currents electrical signature analysis. It aims to present a new non-invasive and non-intrusive condition monitoring system, which has the capability to detect various defects in induction motor at incipient stages within an arbitrary noise conditions. The performance of the developed system has been analyzed theoretically and experimentally under various loading conditions of the motor. Covers current and new approaches applied to fault diagnosis and condition monitoring. Integrates concepts and practical implementation of electrical signature analysis. Utilizes LabVIEW tool for condition monitoring problems. Incorporates real-world case studies. Paves way a technology potentially for prescriptive maintenance via IIoT.