Predictive Maintenance

AI-based Predictive Maintenance Solution

Predictive Maintenance can harmonize disparate internal IoT and non-IoT data, combine it with external data, extract up to 200 relevant features, and provide comprehensive insight into asset risk, thereby, enabling users to maintain higher levels of asset availability across their installed base using proprietary machine learning and computer-vision algorithms.

Use Cases

  • Equipment failure forecasting

  • Equipment component failure forecasting

  • Preventive and regular maintenance planning

  • Financial planning

  • Predicting idle time

  • Prescriptive insights for fixing a potentially faulty equipment

Concentio Serves

Concentio®

AI-based Maintenance Planning solution to reduce Cost

Predictive Maintenance application by Scry Analytics is extremely versatile and can easily ingest data from various data sources and alert operators of probable failure of some or all parts of an equipment so the maintenance could be planned ahead of time saving organizations substantial money and time.

More Detail

Concentio has been built using
the following technologies

  • Bayesian networks

  • Reinforcement learning

  • Generic rule-based machine learning

  • Heuristic algorithms

  • Data mining

  • Big data integration

  • Big data analytics

  • Reverse engineering

Key Differentiators &
Business Benefits

  • Pluggable APIs

    Ready to use data ingestion connectors and APIs.

  • Customizable Alerts

    Customizable real time alerts for critical events in a network of assets based on prescriptive analysis as per client requirements.

  • Failure Forecasting

    Provides visualization of risks across asset portfolios and businesses.

  • Precision

    90%+ precision in predicting potential failures.

  • Impact Analysis

    Performance of individual pieces of equipment based on probability and impact of failure.