Abstract
The popularity of stray and air-gap flux monitoring methods is increasing. This trend is justified by several advantages of such methods over the stator current monitoring that has been demonstrated for electrical fault detection in the induction and synchronous machines. However, the use of the magnetic flux for mechanical faults detections has not drawn this much attention, while in industry, the vibration analysis continues to be popular. This study comes to bridge this gap via the detection of mechanical faults of 6-kV induction motors in a pumping station. The diagnostic procedure mainly involves the stator current and stray flux monitoring, and harmonic index analysis. The localisation of the fault has been made possible via oscillometer readings. It will be shown that mechanical faults have a very different impact on the stator current and the flux signals, while the flux is not sensitive to the bearing fault mechanisms.
Highlights
Electrical machines are the heart of the modern world producing electric power or consuming it to produce mechanical work and products and services
This paper demonstrates the application of a series of diagnostic methods to detect and locate mechanical faults, such as bearing faults and misalignment, in 6 kV induction motors used for water pumping applications
The results demonstrate that the stray flux is not sensitive to actual bearing faults, while the stator current is there superior
Summary
Electrical machines are the heart of the modern world producing electric power or consuming it to produce mechanical work and products and services. Due to their critical role for our sustainability, electrical machines condition monitoring and fault diagnosis has known a significant advancement. This paper demonstrates the application of a series of diagnostic methods to detect and locate mechanical faults, such as bearing faults and misalignment, in 6 kV induction motors used for water pumping applications. Since in electrical faults monitoring, the stator current is not always reliable, for generalised monitoring it is necessary for the two methods to work together but focusing on different failures [26].
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