Maintenance Excellence Through AI/ML Based Solutions.
The preventive maintenance module makes sure that the routine maintenance of the equipment is carried out without any delay to prevent the chances of equipment failure and unplanned downtime. Rubus offers such preventive maintenance that is planned and scheduled based on real time data and insights to keep the assets running efficiently by maintaining a high safety level for the employees, and to avoiding large and costly repairs down the road. Thereby making sure, that the operational disruptions are kept minimum with high operational efficiency.
Enterprise Asset Management (EAM)
Enterprise Asset Management (EAM)is one of the modules offered by Rubus IIOT, to efficiently manage all the physical assets of the industry be it IT or Non-IT assets. It involves asset maintenance, work management, supply chain management, planning and scheduling, etc. Adopting EAM, with integration with predictive maintenance and condition-based monitoring will enhance the asset performance in the industry. This feature of Rubus helps in reducing the downtime, increase the production uptime by optimizing the quality and utilization of the asset.
Condition Based Monitoring(CBM)
Condition based monitoring results in the continuous monitoring the industrial assets using the sensors and the data acquired by it thereby monitoring the asset in real time. All the data from different industrial hardware or equipment are acquired and monitored on real time basis and rules are applied to find exceptions or thresholds that are exceeded. In case of deviations, it triggers actions in the form of alarm, SMS or email or as advanced as the automatic creation of a maintenance order in an Enterprise Asset Management system without any delay.
Rubus offers an effective Predictive maintenance technique that results in the low maintenance frequency by preventing the unplanned reactive maintenance and reduces the cost involved with frequent preventive maintenance. It analyses the real time data from equipment and devices to detect anomalies and leverage historical data to recognize patterns and predict failures before they actually happen. Then it sends alerts or automatically trigger appropriate maintenance processes there by resulting in optimized operations and high quality with the root cause analysis module and best next action workflow. Rubus has ready to use predictive maintenance module for thermal plants, metro, seaports, bottling plants, cement industry, ceramic industry, etc. that are based on different techniques such as anomaly detection, clustering, linear regression, regression tree, etc.