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Nanoprecise software
Prescriptive Maintenance
RotationLF - AI-based Predictive Maintenance Platform
We deploy customized AI based on the plant and equipment type and let it learn for a few days using a mix of supervised and unsupervised learning. This coupled with higher efficiency signal processing and improved IMF separation means faults can be detected in as little as 5 Days!
Energy Centered Maintenance
NrgMonitor - Energy Consumption Tracking System
NrgMonitor is an advanced energy management system designed to track and analyze energy consumption patterns in motor-driven equipment. Utilizing data from MachineDoctor sensors, it provides real-time insights into the power usage of various industrial machines, including motors, pumps, gearboxes, engines, and turbines. Its scalable AI-based software allows integration with existing systems through an open API, facilitating automation and energy efficiency improvements. By offering real-time visibility into energy consumption changes due to equipment faults, NrgMonitor helps organizations identify inefficiencies and take corrective action to reduce operational costs and carbon emissions. This tool is particularly beneficial for industries looking to report on and manage their carbon footprint effectively, ensuring compliance with environmental regulations.
TransformerLF - Smart Transformer Monitoring
NrgMonitor is an advanced solution designed to track energy consumption in motor-driven equipment through data collection in near real-time. By using the MachineDoctor line of sensors, this product provides visibility into power consumption variations in various machines, such as motors, pumps, turbines, and generators, particularly under fault conditions. It highlights inefficiencies that contribute to excess energy use and increased CO2 emissions, aiding regulatory compliance. The AI-based software offers a scalable, cloud-based solution easily deployable across diverse scenarios. Additionally, it supports last-mile automation through an open API. NrgMonitor empowers users to achieve a fast return on investment by identifying which machines are inefficient, thereby optimizing operating expenses. It allows for proactive maintenance strategies and targeted actions to reduce carbon footprints, contributing to cost savings and environmental sustainability.
