Leverage AI to better manage operations and asset maintenance.
Infosys Nia Asset Efficiency Solution ushers in a new approach to planning and management of assets by acquiring, interpreting, and applying contextual knowledge.
Thus simplifying asset operations, maximizing asset lifespan, and amplifying the capabilities of the entire organization.
Infosys Nia combines end-to-end capabilities required for the asset efficiency improvements of Industry 4.0. It extracts asset related data scattered across the enterprise to create a knowledge model to operationalize organizational information, uses Big Data Analytics and Machine Learning to identify issues, perform root-cause analysis, and predict maintenance requirements.
Allows acquisition and transfer of data from industrial systems operating on multiple industrial protocols ...
Multiple adapters to acquire data from various enterprise sources ...
Enables model building using machine learning techniques ...
Connects a set of disparate, unrelated entities and captures the relationship between them ...
Eliminates inefficiencies by automating tasks that do not ...
Allows real-time KPI monitoring, correlation analysis and prognostics ...
A large automobile manufacturer was dealing with unpredictable downtime and unwarranted replacement of spindles based on OEM guidance in one of their manufacturing shop floors.
Infosys Nia ingested data from legacy machines, machines with proprietary protocols, and machines with modern controls through the Infosys Nia M2M gateway. Edge analytics monitored sensors and raised alerts when thresholds were exceeded. Trend analysis and frequency domain analysis were used to predict faults. Infosys Nia automated time consuming data science activities with a framework to run Exploratory analysis, feature extraction, build, test and deploy the machine learning models.
30% reduction in effort to detect anomaly.
Reduced downtime of the spindle machine by predicting the remaining useful life.
Reduced cost of maintenance and production loss due to unwarranted replacement of the spindle.
A large automobile manufacturer was dealing with unpredictable downtime and unwarranted replacement of spindles based on OEM guidance in one of their manufacturing shop floors.
Infosys Nia ingested data from legacy machines, machines with proprietary protocols, and machines with modern controls through the Infosys Nia M2M gateway. Edge analytics monitored sensors and raised alerts when thresholds were exceeded. Trend analysis and frequency domain analysis were used to predict faults. Infosys Nia automated time consuming data science activities with a framework to run Exploratory analysis, feature extraction, build, test and deploy the machine learning models.
30% reduction in effort to detect anomaly.
Reduced downtime of the spindle machine by predicting the remaining useful life.
Reduced cost of maintenance and production loss due to unwarranted replacement of the spindle.