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Condition based maintenance of electrical motors in the mining industry
University West, Department of Engineering Science.
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This project aims to present a condition-based maintenance algorithm for common motor faults on three-phase induction motors in the mining industry through the use of the E300 overload relay. To fulfil this aim establishing what motor faults are common and which ones are suitable for detection based on electrical signal monitoring was explored. Through investigative research no distinction between common motor faults in general and those in the mining industry was discovered. A number of electrical signals candidates were chosen based on limited available research and consulting with experts in the field. To test these signals a number of phase to phase motor faults were physically simulated on a test rig. The resulting trend of the signals were saved and comparatively analyzed with a regular mechanical motor overload to find deviations that would indicate the fault has occurred.

The most useful monitored signal was the current imbalance and the line current measurements. Through monitoring these signals it was concluded that the one phase-pair fault and the two phase-pair fault could be discovered by monitoring real-time values. The three phase-pair fault could not be discovered by monitoring the real-time values, but can be found trough historical trends of the line currents and current imbalance. 

A condition-based maintenance algorithm was proposed utilizing the monitored electrical signals to create trends that can be tied to alarms. A general motor health index that would use the monitored signals to easily convey the health of the motor is proposed. A faceplate design for the Panelview 5510 was designed showing the trends of the various electrical signals that would be used by an operator in the field.

Place, publisher, year, edition, pages
2023. , p. 62
Keywords [en]
Condition-based maintenance, Condition monitoring, Electrical signal analysis, Mining industry
National Category
Robotics
Identifiers
URN: urn:nbn:se:hv:diva-20555Local ID: EXC915OAI: oai:DiVA.org:hv-20555DiVA, id: diva2:1782971
Subject / course
Robotics
Educational program
Master i robotik och automation
Supervisors
Examiners
Available from: 2023-07-19 Created: 2023-07-18 Last updated: 2023-07-19Bibliographically approved

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