Advanced Data-Driven Prediction & Anomaly Detection

  • Predictive Maintenance & Overall Equipment Efficiency (OEE)
  • Maximize System Uptime & Productivity
  • Enhance Production Quality
  • Financial Anomaly Detection, Fraud, Waste

Data Driven Anomaly Detection

AI Edge Sys delivers next generation cloud and on-site AI solutions for behavior prediction and anomaly detection. Our flexible ecosystem is applicable across a wide range of industries, detecting anomalies and anticipating issues before they occur.

Behavior & Anomaly Modeling Advanced Ecosystem

  • AI Edge systems are industry, machine, system and operation agnostic. They are solely data driven, and consume clients side data to build and train custom AI models for their operations.
  • Our API friendly ecosystem offers tools to integrate, format and process big data to deep data and features needed for AI processing.

Data Platform

  • Modeling machines or systems behavior and detecting anomalies require a vast array of data, often scattered across physical and digital infrastructures. E.g., machine sensors, PLCs (programmable logic controllers) and CMMS (computerized maintenance management system), or production, operation, financial and transaction management systems. Integrating and structuring these diverse data in uniform formats suitable for extracting valuable features and insights for AI behavior modeling and anomaly detection represents a significant challenge for any enterprise.
  • AES Data Hub is an API friendly data platform offering numerous tools to quickly create custom pipelines for data gathering, ingestion, integration and processing. Robust data transformation capabilities ensure that raw sensor and operational data are transformed into structured formats ready for analysis and AI modeling.

Financial Anomaly

AES Predictor provides advanced solutions to detect fraud, waste, manipulation and collusions in the financial activities of agents operating within a platform.

  • Public and private insurance firms processing claims from patients and providers.
  • Financial companies monitoring insider fraud, theft and data manipulation.
  • Energy and financial market operators and regulators looking for insider trading, collusion and market manipulation.

Predictive Maintenance

AES Predictor solutions deliver enhance predictive maintenance planning & Overall Equipment Efficiency (OEE), maximize systems uptime & productivity and improves production quality.
  • Clients: smart Industry 4.0 plants, Industrial Internet of Things (IIoT), tradition plants, power plants, petrochemicals plants, all size manufacturing, agrofood production, technologies, automotive, consumer goods or pharmaceuticals
  • Data: the AI model integrates and use all possible data in your operations, smart plants, Industrial Internet of Things (IIoT), machines’ analogue and digital sensors, PLCs (programmable logic controllers) and CMMS (computerized maintenance management system).
  • System outputs: We compute a comprehensive metrics: MTBF (mean time before failure), MTTR (mean time to recovery, repair, respond, or resolve), MTTF (mean time to failure).

Sensors Anomaly

Harness AI to predict system anomalies, utilizing high-frequency sensor data. Automate issues detection, optimize maintenance, and ensure seamless production.

  • AES Predictor detects anomalies and deviations in systems behavior before they occur, take hold and disrupt production.
  • The deep learning and machine learning models make extensive use of high-frequency sensors, SCADA, PLC and CMMS data, e.g, vibration, sound, electrical measurements, temperature, pressure, volume, level, etc.
  • Automate root-problem identification, minimize production disturbances, and optimize maintenance planning.

Quality Management

AES Predictor can be used in two modes. Either detect anomalies and deviations in upstream production processes before they negatively impact the end-product quality, or, analyze data readings from final products to detect defects.

  • Identify product defects, minimize production waste and quality variation across production batches and series.
  • The deep learning and machine learning models make extensive use of high-frequency sensors, SCADA, PLC and CMMS data.