The industrial internet of things (IIoT) has arrived.
MonoM was born in 2016 as a company of the Alava Group, with more than 45 years of experience implementing high-tech solutions for the improvement of processes and profitability in the industry.
MonoM, aims to create an IIoT platform for predictive maintenance based on artificial intelligence applied to Industry 4.0.
The Cliff was responsible for developing the BackEnd of the entire platform in a microservices architecture based on Google Cloud, implementing the frontEnd layer and its deployment on MonoM servers.
Creating digital twins of any industrial asset: the core of the revolution.
The IIoT platform developed allows you to create a complex digital twin of any Industrial nature (from production plants to small sensors that control the temperature of an engine gear) to have complete visibility of all your assets, with tools that facilitate the understanding of your processes and your machines.
In addition, it converts the data generated by this system into organized, understandable and optimized knowledge. This makes it possible to know what is happening in the plant at any given moment and to predict the status of the recorded assets.
Connecting real data with digital copies, the key to success.
MonoM allows to establish any connection between a physical device and a digital twin asset, connecting the real data with the digital copy. Once connected, the generated data can be visualized and organized in different ways: from the most visual with geopositioned node maps to the most classical lists of elements through hierarchical views.
Each Asset in its detail shows its static data (its characteristics provided to create the item in the digital Twin) as well as its dynamic data (those received through the connected sensors). Historical and analytical dashboards can be accessed for any sensor.
Once the entire plant has been digitized and connected, through an alert and notification manager, advanced alerts with complex logic and sections can be configured in order to audit any anomaly or behavior in the data of the connected devices and thus be able to meet the objective of monitoring the entire plant for optimal predictive maintenance.