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Monitoring Intelligence

Condition Monitoring

Condition Monitoring provides businesses with a powerful tool to foresee potential equipment failures, offering up to 24-hour predictions on performance. This predictive capability enables operations teams to proactively address issues before they result in unplanned downtime. By eliminating unexpected breakdowns, companies can maintain optimal plant production, reduce costs, and enhance operational efficiency.

Condition Monitoring leverages advanced AI models running hourly on Windows Servers to predict equipment conditions accurately. These models continuously improve by analysing historical performance data, autonomously updating themselves to enhance predictive accuracy. The system’s lightweight and scalable design makes it ideal for diverse industrial applications, offering real-time insights without requiring supervised training, ensuring both precision and adaptability.

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Condition Monitoring

Anomaly Detection

Anomaly Detection safeguards operations by identifying unusual equipment behaviour early, enabling maintenance teams to take corrective action before failures occur. This proactive approach reduces unplanned downtime, enhances production reliability, and minimises repair costs. By transitioning from reactive to predictive maintenance, businesses gain a strategic edge in maintaining operational continuity.

The system employs self-improving AI models that detect anomalies in real time, flagging deviations from expected equipment behaviour. Running on Windows Servers (without specialized hardware), these models update hourly, refining their accuracy using historical performance data. Its scalable, unsupervised learning approach ensures continuous adaptation to equipment changes, delivering timely and actionable insights for effective predictive maintenance.

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Anomaly Detection

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