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.
Equipment failure forecasting
Equipment component failure forecasting
Preventive and regular maintenance planning
Predicting idle time
Prescriptive insights for fixing a potentially faulty equipment
& equipment firms
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
- 30+ proprietary AI-based algorithms
- Automated ontology - updated dynamically
- Uses advanced machine learning algorithms to compute failure risk scores
- Time-series and frequency domain analyses
- Analyzes videos and images to detect potential faults
- Real time alerts for anomalies
- “Human in the loop” helps the software in improving its accuracy on a continuous basis
- Pre-built connectors to online platforms and sources for enhancing anomaly detection with external data
- Provides trade-offs between time and cost for fixing an equipment
- Reduced operational costs by shifting to predictive maintenance
- Reduced downtime due to early identification and upgrade of equipment likely to fail
Concentio has been built using
the following technologies
Generic rule-based machine learning
Big data integration
Big data analytics
Key Differentiators &
Ready to use data ingestion connectors and APIs.
Customizable real time alerts for critical events in a network of assets based on prescriptive analysis as per client requirements.
Provides visualization of risks across asset portfolios and businesses.
90%+ precision in predicting potential failures.
Performance of individual pieces of equipment based on probability and impact of failure.