SubseaAI — Real-Time Pipeline Leak Detection

SubseaAI – Full Engineering Report 1. Abstract

SubseaAI is a real-time multi-sensor AI system designed to detect and track leaks in subsea oil and gas pipelines. The system integrates thermal, acoustic, and pressure data using probabilistic fusion and temporal tracking to improve reliability under noisy underwater conditions.

1. Problem Statement

Subsea pipelines operate in harsh environments where:

Small leaks remain undetected Sensor data is noisy and unreliable Traditional systems rely on single-sensor logic

This leads to:

Environmental damage High economic losses Delayed response times 3. Objectives Detect leaks early in real-time Reduce false positives Handle noisy and incomplete sensor data Build a scalable and modular system 4. System Architecture 4.1 Overview

The system is divided into five layers:

Data Acquisition Preprocessing Sensor Fusion Tracking Output & Alerts 4.2 Data Acquisition

Sensors used:

Distributed Temperature Sensing (DTS) Hydrophone (Acoustic) Pressure sensors

Each sensor provides time-series data with noise and uncertainty.

4.3 Preprocessing

Steps:

Noise filtering Normalization Time synchronization

Goal: Convert raw signals into structured inputs.

4.4 Sensor Fusion

Approach:

Combine multiple uncertain signals Assign confidence levels to each sensor Merge into a unified belief score

Benefit:

Reduces dependency on any single sensor 4.5 Tracking Layer

Method:

Temporal smoothing of signals Continuous estimation of leak state

Purpose:

Avoid sudden spikes and instability Track leak progression over time 4.6 Output Layer Leak detection alerts Confidence score Location estimation (if applicable) 5. Data Flow

Sensors → Preprocessing → Fusion → Tracking → Decision → Alert

1. Implementation 6.1 Technologies Used Python Signal processing libraries AI-assisted development tools 6.2 Design Approach Modular pipeline Real-time processing focus Scalable architecture 7. Key Challenges Handling noisy underwater data Synchronizing multi-sensor inputs Maintaining real-time performance 8. Results Successful prototype implementation Real-time detection achieved Improved stability compared to single-sensor systems 9. Limitations Requires real-world deployment data Sensor failure affects performance Optimization required for edge deployment 10. Future Work Edge deployment Integration with industry systems Improved robustness and latency reduction 11. Conclusion

SubseaAI provides a reliable and scalable approach to subsea leak detection by combining multi-sensor data and temporal tracking. It improves detection accuracy and reduces uncertainty in challenging environments.

1. References Signal Processing Concepts Sensor Fusion Methods Real-time System Design Principles

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Built With

• python