INTELLIGENT CONDITION MONITORING SYSTEM FOR PREDICTIVE MAINTENANCE ON DC PUMP / GIDEON JGORDON ANAK ISA.
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TextPublication details: Kuala Lumpur : Asia Pacific University, 2019Description: xiv, 114 pages : illustrations ; 30 cmSubject(s): Machinery -- Monitoring | Cavitation | Fluids -- MechanicsLOC classification: PG-22-0060Dissertation note: A project submitted in partial fulfillment of the requirement of Asia Pacific University of Technology and Innovation for the Degree of B.Eng (Hons) in Mechatronic Engineering (UC4F1811ME). Summary: In water distribution system, a breakdown in pump can lead to service disruption in the ware distribution services. The consequences would lead to a hampering in the daily lives of user dependent on the pump to deliver water. The main cause of pump breakdown can be attributed from a phenomenon called cavitation. Cavitation is a slow process and mostly unnoticeable by naked eyed as it occurred in the impeller of the pump. This paper proposed a condition monitoring system integrated with of Machine Learning and IoT for predictive maintenance of the pump. The project was successfully commission in several pumps for predictive maintenance.
| Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode |
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Reference
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APU Library Reference Collection | Undergraduate Theses | PG-22-0060 (Browse shelf (Opens below)) | 1 | Not for loan (Restricted access) | 00018421 |
A project submitted in partial fulfillment of the requirement of Asia Pacific University of Technology and Innovation for the Degree of B.Eng (Hons) in Mechatronic Engineering (UC4F1811ME).
In water distribution system, a breakdown in pump can lead to service disruption in the ware distribution services. The consequences would lead to a hampering in the daily lives of user dependent on the pump to deliver water. The main cause of pump breakdown can be attributed from a phenomenon called cavitation. Cavitation is a slow process and mostly unnoticeable by naked eyed as it occurred in the impeller of the pump. This paper proposed a condition monitoring system integrated with of Machine Learning and IoT for predictive maintenance of the pump. The project was successfully commission in several pumps for predictive maintenance.
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