“Customer Outcomes” drive our Technology decisions. Applications like Predictive Maintenance or Process Optimization are built with a deep vertical-specific focus.
Machine Learning Framework
The Machine Learning framework is designed to carry out a sequence of analytics-intensive functions like validation, statistics, predictive models and risk evaluation on any streaming data.
Uses self-learning models to understand asset/process behaviour from historic data
Auto detection of poor quality or erratic data and substitute plausible data values instead
APR Algorithms which mine for relationships and trace abnormal patterns and data deviations
Models for early detection of issues and forecast Remaining Useful Life of Assets
Models built based on first principles and physics to help validate actual asset performance
Provide diagnostics through fault trees or perform automated RCA using data
Calculate a reliability index of every single asset based on a comprehensive check of all parameters
The Asset Library is a collection of Digital Twins of over 50 asset types with a deep understanding of their failure modes, diagnostics and corrective strategies.
Connect all seemingly disparate plant systems to derive increased contextual insights about your Assets. Pulse is designed for industry scale data and allows you to onboard as many Plants and Assets with ZERO concern about IT Infrastructure and Security.
Designed for the Industries, Pulse aggregates petabyte-scale sensor data from widely used automation systems like DCS, PLC, SCADA using standard and secure protocols (like OPC). This can also be augmented with video, audio, location data as well as data from enterprise systems like maintenance management and ERP to have a complete view of the health and history of any asset.