Machina Aeternum : Predicting the Unpredictable

Machina Aeternum is Peakey Enterprise's flagship predictive maintenance platform. Built on our patent-pending technology (originally introduced in our 2021 whitepaper "Predicting the Unpredictable"), it combines multivariate time-series forecasting with intelligent anomaly detection to identify degradation, failures, and "false anomalies" - events that look random to humans but are actually predictable when all sensor relationships are modeled together. Unlike traditional threshold alerts or basic anomaly detectors, Machina Aeternum:

• Pulls data securely from platform, database, IoT, or custom implementation.
• Forecasts future sensor values across multiple streams
• Runs anomaly detection on both current and predicted future states
• Triggers automated alerts (email, SMS, automatic shutoff, or any custom implementation) when issues are detected
• Supports custom pre-defined rules for your domain-specific events

All processing can be performed locally on your infrastructure - perfect for organizations that require data sovereignty and offline operation.

• Real-time / Batch Anomaly Detection: Flags deviations as they occur.
• Multivariate Forecasting: Predicts how multiple sensors will behave together over configurable time horizons.
• Predicted Anomalies: View emerging risks before they happen; full automated predictive alerting available.
• Human-in-the-Loop Refinement: Low-confidence events escalate to your experts for labeling, continuously improving accuracy.
• Hybrid Rules Engine: Combine ML with your custom hard-coded logic for critical events.
• Extensible & Integratable: Easy connection to databases, notification systems, ticketing tools, and TensorBoard for model monitoring.

• Up to 50% reduction in unplanned downtime
• 25-40% lower maintenance costs
• Improved safety, reduced scrap/waste, and higher asset reliability

Real-world example outcomes include preventing machine failures, maintaining quality thresholds, optimizing throughput, and avoiding service outages on everything from industrial valves to fleet equipment.

Machina Aeternum builds directly on Peakey's (Predicting the Unpredictable: Implementation of an Advanced Machine Learning Method to Predict Degradation and Failure using Multivariate Time-Series Forecasting @ 2021).
Our approach solves two major challenges in industrial AI:

• Self-supervised learning: No massive manual labeling required upfront.
• False Anomaly Prediction: Identifies events that appear anomalous but are predictable when all dependent variables are modeled together.

In independent testing on public industrial datasets (e.g., SKAB valve data), our models successfully predicted sharp anomalies - such as a dramatic drop in flow rate RMS - nearly 10 minutes in advance using only seconds of prior data.

• Can run fully offline on your servers - zero customer device changes needed
• Starts simple and scales with your needs (pilot to full 1M+ device deployments)
• Hybrid ML + rules approach ensures nothing critical slips through
• Future-ready: The base solution already includes a preview of predicted anomalies to show the power of forward-looking intelligence

We offer low-risk pilot implementations tailored to your setup. Many customers start with 30-50 devices and expand rapidly once value is proven. Contact us today to discuss a pilot or request the full whitepaper.