Machines, assembly lines and continuous or batch production processes generate lots of sensor data and digital I/O data.

It is impossible for humans to continuously monitor all that data to detect issues with the production assets.

Since 2011, Yazzoom helps its customer to extract valuable information from their production data.

YANOMALY™ is our software product that enables you to use that data for real-time monitoring of the condition of your assets through anomaly detection and predictive modeling using high performance proprietary algorithms specifically developed for industrial data.

This is highly valuable for production and maintenance managers and professionals to improve the overall equipment effectiveness (OEE) in all its aspects: availability, performance and quality of the production. It helps you move from reactive maintenance to data-driven maintenance where that makes sense.

Yanomaly has generic AI-based anomaly detectors and predictors that can be applied to any asset or production process. It also has specific anomaly detectors for specific often occuring assets like motors, pumps and control loops and that incorporate both human expertise about failures in those assets and AI-based algorithms to learn the specifics of individual assets.

With its modular highly scalable architecture and flexible licensing, YANOMALY can be easily integrated into existing production monitoring solutions, and can be coupled with asset management software.

YANOMALY has a web browser-based graphical user interface that enables non-programmers to create, deploy and maintain those asset health monitoring models.

One can define alerts that will notify the right people with the appropriate actionable message in case specific health issues are discovered. Yanomaly always gives precise information about the reason for an alert.

It includes dashboards and alert lists that show the health status of the assets in your factory(ies), and reports that show summary statistics of the health status of selected assets in selected periods.

Yanomaly differs from traditional condition monitoring, that monitors the health of specific equipment, often using vibration sensors, in three ways:

1) It uses available process data like electrical current, speed, temperature etc. to detect abnormal behavior, and optionally, when available, also integrates features from vibration sensors or motor current signature analysis.

2) It uses machine learning to take into account the specific behavior of the individual asset, and the context (operating condition) in which the asset operates. Without requiring asset specific configuration work.

3) It can be used to discover anomalies on the system or process level, as well as on the level of individual assets and sensors.

Yanomaly is used by SMEs and large companies in a variety of industries, ao. : Engie-Laborelec (power), Stora-Enso Langerbrugge (pulp & paper), Agfa (chemical), Tenneco (automotive), TE Connectivity (automotive), Algist Bruggeman (food),...