PREDICTIVE MAINTENANCE USING MACHINE LEARNING ON WATER PUMP









Abstract

Predictive Preservation or maintenance is used in conjunction with the Internet of Things to assist the industry in detecting important faults in production or maintenance devices. In this study, we offer a system architecture model for detecting early water pump system failures based on data acquired by monitored controlled devices. We worked on real measured statistics, events, and failures from the water pumps sector in the tentative portion. Different preservation measures or tactics are being tried to maintain the industries' effectiveness. Preservation disrupts the value of goods works in any sector. To avoid being shocked or later breaking down, preservation procedures should be planned in such a way that the preservation task is reduced in both cost and time. This research study describes the implementation of a keen and Machine Learning architecture system for Predictive Maintenance or Preventive Preservation, based on the Random Forest method in an Industry sector, that considers the Internet of Things and Machine Learning (ML) technologies to support real-time statistics, an online collection of data, and analysis for detecting machine breakdowns sooner, allowing real-time monitoring on the data, visualization of the data, and analysis for detecting machine breakdowns earlier.


Modules


Algorithms


Software And Hardware