Yanomaly AI-Powered Analytics for Industrial Data Platforms


Machines, assembly lines, packaging lines and continuous or batch production processes generate more and more data that is full of hard to extract but valuable information.

YANOMALY enables you to use that data for real-time monitoring of the condition of your assets through AI-powered anomaly detection, to troubleshoot technical faults or production issues thanks to advanced diagnostic analytics, and to build predictive or prognostic models using proprietary machine learning algorithms specifically developed for industrial data.
With its modular highly scalable architecture and flexible licensing, YANOMALY can be easily integrated into existing monitoring platforms, in the cloud or on premise.

Ease of Use: Yanomaly includes web-based GUI to enable non data scientists to select data, train the computer models, and to validate, deploy, and monitor and maintain those models. Users can configure alarms and/or see the status overview dashboard, and can do a drill down to further analyze the causes of process issues or figure out how to improve quality.

AI-powered Functionalities
* Anomaly Detection: 80 percent of downtime is caused ​by never-seen-before ​technical or process issues. YANOMALY doesn’t need an issue to have already occurred in the past to detect it.

* Diagnostic Analytics: YANOMALY’s diagnostic analytics module allows you to identify the main factors that influence key performance indicators and metrics.

* Predictive Models: Predictive modeling (virtual sensors) allows you to build machine-learning models and deploy them in production.

Built to be interfaced or integrated with existing data monitoring or IoT platforms, YANOMALY can process all types of data from sensor signals and other time-series data to event logs of machines, categorical data coming from ERP and MES systems and more.

Maximize results under economical and operational constrains by leveraging Yazzoom’s expertise in optimization algorithms and advanced process control.





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