Deftly detecting anomalies from sensorbased multivariate time series
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- Deftly detecting anomalies from sensorbased multivariate time series
Technology Overview
The CAD system detects anomalies by analyzing sensor correlations in machines, outperforming benchmark methods in accuracy and speed. It supports real-time
monitoring for 1,000+ sensors, making it ideal for large-scale industrial use.
Correlation analysis-based Anomaly Detection (CAD) system
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Correlation-Based Intelligence:
Detects anomalies by analysing sensor-to-sensor correlations, not just individual sensor thresholds. Captures hidden machine interactions and interdependencies
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Earlier Fault Detection:
Outperforms benchmark methods by identifying abnormal time periods and affected sensors with fewer false alarms.
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Real-Time Performance:
Delivers rapid anomaly detection while maintaining robust and reliable results.
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Scalable for Large-Scale Industrial Systems:
Supports real-time monitoring of 1,000+ sensors, making it suitable for complex, enterprise-level machinery.
Maritime Use Cases

This AI-driven system analyses engine performance, fuel consumption, and mechanical stress data to detect early signs of equipment failure, helping prevent costly breakdowns and improve operational reliability.

The system can also monitor port equipment such as cranes, automated guided vehicles (AGVs), and cargo-handling systems in real time, reducing unexpected downtime and improving operational efficiency.

In addition, it can support offshore operations by enabling continuous monitoring of assets such as wind turbines, oil rigs, and floating storage units, allowing potential failures to be identified and addressed before they escalate.
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