Preventive Maintenance

Apply the power of AI to effectively and precisely

  • Predict failure and potential issues before they occur.
  • Prevent unplanned downtime and minimize loss of revenue.
  • Maximize Overall Equipment Efficiency (OEE).
  • Minimize downtime, disruption and maintenance cost.
Contact us to explore a free POC trial.

AI Preventive Maintenance Management

AIPMP the AI Preventive Maintenance Predictor employs specialized AI algorithms which use extensive sensor data to predict equipment failure, enhance predictive maintenance planning and Overall Equipment Efficiency (OEE), while minimizing disruptions.

The solution can be of interest to Smart Industry 4.0. Tradition plants, e.g., power, petrochemicals, cement. All size manufacturing and production, e.g., agrofood, technologies, automotive, consumer goods or pharmaceuticals. Industrial Internet of Things (IIoT).

Advanced AI Behavior & Anomaly Modeling 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 a powerful data platform which automated the integration, formatting and processing of big data to deep data and intelligent features for AI processing.

AIPMP the AI Preventive Maintenance Predictor generates a multidimensional maintenance metrics

  • MTBF: mean time between failure.
  • MTTF: mean time to failure for non-repairable or replaceable product.
  • MTTR: mean time to recovery or restoration from a system failure or outage.
  • RUL: remaining useful life.


Features of AIPMP the AI Preventive Maintenance Predictor

Features-Preventive Mentenece


Benefits of AIPMP the AI Preventive Maintenance Predictor

Predict failures

Accurate failure predictions

Predict machine and component failure to optimize the planning and timing of maintenance and repairs to periods when revenue losses are the lowest.

Turn unplanned to planned downtime and preserve revenue stream

Prevent unplanned downtime to maximize production revenue and minimize losses, while meeting production constraints.
Prevent unplanned downtime
Optimize supply chain

Plan for optimal maintenance timing

Use accurate failure prediction to optimize maintenance schedule, the timing of ordering parts, all with the objective to minimize maintenance costs.

Achieve supply chain resilience

Optimize the resilience of your supply chain and Predict patterns of failure to maximize Overall Equipment Efficiency (OEE) health, productivity and the remaining useful life of parts and components.
Predict patterns of failure
Predict failure proactively

Maximize Overall Equipment Efficiency and health

Predict failure proactively and prevent the propagation of malfunctions to other functions and components.

AIPMP the AI Preventive Maintenance Predictor does not model equipments and machines from an engineering view-point, it rather creates a customized data driven synthetic AI model using detailed telemetry sensor data and event logs. E.g., analogue and digital sensors, PLCs (programmable logic controllers) and CMMS (computerized maintenance management system), smart plants and Industrial Internet of Things (IIoT) data stream. Our platform gathers, transforms, structures and manage these data in a hyper-performance data hub.

From Sensor Big Data to Deep Data, We Accelerate Transition to Intelligent Digital Enterprise

AIPMP the AI Preventive Maintenance Predictor has built-in data processing & feature extracting toolboxes especially designed to apply a range of mathematical transformations to quantify and extract features from sensor and event logs data as preparatory inputs for AI anomaly detection modeling.

Sys Edge Data Hub is a modern API and web- friendly data platform that creates, via simple configuration, robust and flexible data fetching, mapping and management processes alongside custom data models for complex telemetry, sensor and events logs. 

These data have disparate timestamps, formats, and types. They are too large in size, lack indexing and structuring for feature extraction an AI modeling. They are processed and transformed to ensure efficient and seamless access via API for AI modeling, deep analytics, reporting, and third party use.

Sample sensor data

Sample event log data

Sample metadata

Brand, models, age, unit or department, geospatial data, GPS coordinates, IP address, movement tracking, path.