Condition Based Maintenance
Maintenance plays a vital role in optimizing an asset's performance and achieving efficiency. However, maintenance actions are always associated with time, resources, materials, and cost. Also, often critical machinery costs are higher in their breakdowns and scheduled maintenance downtimes. Frequent maintenance of Machineries increases operational costs while scarce maintenance can result in unexpected downtimes and compromised efficiency. Hence it is essential to optimize these actions and bring up optimized maintenance instances.
Modern industrial machineries are highly complex and are equipped with advanced technologies to meet their demands. Most of the manual works are automated using automation and technology resulting in the production of vast streaming data. This is where we can leverage the use of Artificial Intelligence for Maintenance management. The field device information from various sensors and actuators will be analyzed to predict the requirement of Maintenance and breakdowns in an asset helping the maintenance teams to optimize the process and reduce downtimes.
Every piece of equipment and its components are designed to run at specific conditions and at an optimized efficiency level. There are specific parameters associated with their designed running conditions and they change with respect to maintenance practices, operational conditions, Equipment running hours/age, losses, etc. These parameters include but doesn't limit to Vibrations, Temperature, Differential Pressure, Noise levels, frictional losses, and Energy consumption. The historical data associated with these parameters, failures, and efficiency analyzed with proper AI models can provide a robust solution to predict the Maintenance requirements and breakdowns. Operational trends analyzed with suitable statistical methods for time and running cost conditions can provide equipment life analysis, optimal equipment replacement solutions, etc. Which can further improvise asset management activities.
The advancement of Equipment, Production technologies, Automation, and others are generating a huge amount of data that can significantly improvise maintenance management activities and provide the finest solutions for cost optimization and reducing downtimes. Modern CMMS solutions with analytical capabilities can become a boon to the industry and help in achieving KPIs.
At Desapex, We focus on the Integration of advanced technologies to achieve sustainability by providing an end to end-digitalization solutions for built assets. We are partnered with Finnish technological experts 'Granlund' to offer Building Analytics and Smart maintenance management solutions in the Indian subcontinent through a SAAS CMMS solution called "Granlund Manager". The Dynamic inputs received from Various BMS, EMS, and IOT systems are analyzed and the performance of assets are calculated based on pre-defined Conditions set by the experts.
Analytics help us to evaluate how well an asset is performing and the requirement of maintenance actions.
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