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.
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.
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
Features of AIPMP the AI Preventive Maintenance Predictor
- Machine and components failure are complex and interlinked and can’t often be detected in isolation. They can rather be identified in lateral shifts when considered across several failure categories, levels or stages.
- A second layer meta-learner AI implements scoring that assesses and ranks failures by different risk metrics, enabling focused attention on high-risk cases.
- Set custom alert thresholds for different machines or components ensuring relevant notifications are issued to avoid information overload.
- Root cause identification and forensic analysis is enabled through detailed reports to deepen your understanding of failure factors.
- In back-testing & scenario mode, the AI model can be executed on sensor and event data with the addition of what-if-scenarios to measure the effectiveness of the AI failure detection model, and to enhance your ability to predict future failures.
Benefits
Benefits of AIPMP the AI Preventive Maintenance Predictor
Accurate failure predictions
Turn unplanned to planned downtime and preserve revenue stream
Plan for optimal maintenance timing
Achieve supply chain resilience
Maximize Overall Equipment Efficiency and health
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.
- The configuration of a custom AI anomaly detection model for a company begins with a custom feature extraction model in formats that are suitable for actionable AI modeling according to groupings aligned with a company’s specific machines, units and operations.
- Custom feature contextualization and enhancement adds valuable nuances and insights into machine, components and entities behavioral patterns.
- Feature mapping creates features across entities: point features are quantified per single entity, peer grouping features are quantified across a group of peers in the same category, horizontal features are quantified across multiple categories.
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
- IT: CPU, memory and disk usage, temperature, fan speed, network latency, transfer rates, packet loss, bandwidth usage, connection status.
- Environmental: machine internal and external temperatures, humidity, vibration amplitude and frequency, air or fluid pressure.
- Operational power status on/off, battery level, error and trouble codes. Diagnostic trouble codes indicating issues.
- Machines: real-time and cumulative power consumption, fuel consumption, rates, wear and tear Indicators.
- Robots: Joint positions, torque, speed, error states.
- CNC Machines: spindle speed, feed rate, cutting tool status.
- Cars: speed, fuel level, tire pressure, engine status, engine components sensors.
- Aircraft: altitude, airspeed, engine & components performance, avionics status.
Sample event log data
- Operational mode, idle, active, off, testing, maintenance.
- Alerts and Alarms: notifications of abnormal conditions.
- Event Logs: detailed logs of significant events or changes.
- User Actions: records of user interactions with the machine.
- Maintenance logs: records of maintenance activities.
