Innovation Overview

At I-care, we are committed to driving innovation and technological excellence to help our customers lower production costs while increasing output and efficiency.

SMART-R4F

Next-generation sensors

  • New AI-enabled IIOT sensors
  • Next Generation certified portable data collector for the Wi-care ecosystem

PROPHESY

A platform for rapid deployment of self-configuring and optimized predictive maintenance services

  • Catalyst for uptake of next-generation, optimal, adaptive, and self-configurable PdM services.
  • End-to-end development, deployment, and operationalization of adaptive self-configurable PdM services.

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 766994.

PEPS

Pumped Electricity Plant Solutions (PEPS)

  • Satisfying emerging need for energy storage technologies
  • Innovative modular concept, easily reproducible, piloted and monitored remotely in a 4.0 approach of operations and maintenance

LightSens

Using Optic Fibers for predictive maintenance

  • Compatible with extreme industrial environments
  • Long distance sensors
  • Distributed sensors

TRACY

Investigating log data generated by industrial assets and refining existing AI and machine learning techniques targeted at time series analysis.

  • Challenges: handle heterogeneity of the data and lack of standardization
  • Validated on industrial use cases: Optimizing the performance of compressors and Decreasing the service cost of electrophotographic machines

CONSCIOUS

(*Contextual aNomaly deteCtIon for cOmplex indUstrial aSsets)

Bringing IIoT and AI to Industrial use cases

  • Correlating production and predictive maintenance data with AI algorithms
  • Integrating IIoT devices to improve data collection

PI-AI

4.0 Modular Industrial Platform

  • Robust and reliable platform to enable 4.0 products management on I-care’s I-see™ software platform
  • Modular and scalable

ACMON

Easy deployment, maintenance and validation of AI models for acoustic monitoring

  • Creating Condition Monitoring based on acoustics
  • Increasing signal-to-noise ratio of sound recordings
  • Training AI models for robust detection of problems