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Yanomaly Diagnostics Tool

YANOMALY is an Industry 4.0 industrial predictive analytics software solution that enables you to use that data
• for real-time monitoring of the condition of your assets through sensor validation and proprietary AI-powered anomaly detection specifically developed for machine health monitoring
• to troubleshoot technical faults or production issues thanks to advanced diagnostic analytics,
• to build predictive or prognostic models using high performance machine learning algorithms
• for implementing and deploying predictive maintenance solutions tuned to your needs by combining best of AI-capabilities and human expertise

Customers of YANOMALY include process and manufacturing production companies, machine builders and IoT platform developers.

YANOMALY has a proven track record on a variety of machine and data types: industrial production lines (continuous and batch processes, as well as discrete manufacturing assembly and packaging lines), medical imaging equipment, IoT-enabled utilities networks.
YANOMALY is not yet another Industrial IoT platform. Instead it is a plug-in software that is used to add advanced data analytics capabilities to any existing machine data collection system like data historians, SCADA/DCS or cloud platforms.

Unique capabilities and functionality


ANOMALY DETECTION YANOMALY doesn't need an issue to have already occurred in the past to detect it. It can learn normal behaviour of sensors, control loops, equipment, machines and processes and detect deviations from that normal behaviour.

DIAGNOSTIC ANALYTICS YANOMALY’s diagnostic analytics module allows you to identify the main factors that influence key performance indicators and metrics.

PREDICTIVE MODELS Predictive modelling allows you to build machine-learning models that predict failures (prognostics) or product properties (virtual sensors) and deploy them in production.

DYNAMIC CENTERLINING AI-based multivariate centerlining helps you identify important process variables and how they interact, determine the best settings and ranges, by grade or product. Ensure the process runs with the centerlined settings through alerts, report and automatic control.

FROZEN PROCESS PARAMETERS MONITORING This self-learning functionality detects changes in frozen process parameters and signals any modification. It can for example alert you when certain variable that are supposed to be changed only during maintenance are modified outside of maintenance operations, or quickly determine which parameters where modified before a problem occurred.

Main Use Cases

OPERATIONS & MAINTENANCE Real-time machine health monitoring with early warning of technical issues, prediction of failure for predictive maintenance and better OEE. Continuous improvement thanks to diagnostic analytics.

SERVICE & SUPPORT Faster diagnostics & root-cause analysis thanks to AI-assisted data exploration, for lower MTTR & more efficient service teams.

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