Purposeful Artificial Intelligence for Asset Efficiency
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.
Features
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.
M2M Gateway
Allows acquisition and transfer of data from industrial systems operating on multiple industrial protocols
Data Ingestion
Multiple adapters to acquire data from various enterprise sources
Big Data Analytics and Machine Learning
Enables model building using machine learning techniques
Knowledge Management
Connects a set of disparate, unrelated entities and captures the relationship between them
Automation
Eliminates inefficiencies by automating tasks that do not
Insights, Correlations and Prognostics
Allows real-time KPI monitoring, correlation analysis and prognostics
Asset Efficiency with Infosys Nia
Choose Your Area of InterestInfosys Nia Delivers Results
Decline in average time for issue resolution
Reduction in energy cost
Reduction in effort to detect the anomaly
Chiller Efficiency Management
Motivation
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.
Solution
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.
Benefits
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.
Spindle Performance Monitoring
Motivation
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.
Solution
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.
Benefits
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.
