High-fidelity distributed sensing (HDS) leverages immense volumes of raw data generated via continuous, integrated measurement of long linear assets. Transforming this data into actionable insights requires massive effort—often far more than a typical in-house team could handle. This is where artificial intelligence (AI) and machine learning (ML) can shine, providing the necessary horsepower to drive cutting-edge sensing software.
Here’s how advancements in these technologies help industries elevate asset management, improve leak detection, and achieve unparalleled accuracy in pipeline (and other linear infrastructure) monitoring.
Processing Vast Raw Sensor Data into Insights
High-fidelity fiber optic sensing platforms generate extensive amounts of raw data, gathering acoustic, thermal, and strain signatures along every inch of the asset continuously 24/7/356. To provide a sense of the true ‘Big Data’ nature of these applications, an HDS system monitoring a 100-km section of active pipeline can generate 10 to 20 terabytes of raw optical data every single day. Scouring such vast datasets in real time while accurately and consistently differentiating real events of interest from a wide range of normal operating conditions is a monumental task. As a result, advanced tools are necessary to filter noise, correlate datasets, and extract actionable insights. AI and ML systems confidently identify critical anomalies and streamline data interpretation for more reliable decision-making.
Orthogonal Data Processing for Accurate Correlation
Orthogonal data processing integrates acoustic, thermal, and strain signals to dramatically increase confidence levels in pattern recognition relative to single-parameter methods. This approach aligns disparate datasets to provide a unified view of asset conditions, effectively comparing or corroborating independent indicators to avoid missed or mischaracterized events. AI ensures precise correlation of signatures, which is critical for accurately capturing everything from pinhole leaks to confidently locating pipeline inspection gauge tools (pigs) which have unexpectantly stopped moving inside the pipe, and a host of applications in between.
Reducing False Positives and Predictive Maintenance
Machine learning algorithms refine anomaly detection by identifying critical events earlier and with great accuracy. In other words, ML continuously analyzes high-quality fiber data, comparing it to historical behaviour for the asset as well as to the enormous catalogue of similar integrity events gathered across Hifi’s extensive network of monitored infrastructure. For pipelines, this approach opens the door to preemptive integrity management, whereby the recognition and timely communication of integrity threats offer the potential to avoid failures altogether. But perhaps the true power of Hifi’s advanced AI & ML processes lies in their ability to virtually eliminate false alarms, providing operators with reliable, data-driven intelligence about their assets while minimizing the resource drain of misguided or unnecessary field checks and investigations.. With this level of confidence, companies employing HDS can benefit from efficient day-to-day asset protection while also proactively supporting longer-term maintenance schedules and integrity planning.
Real-World Applications of AI-Enhanced HDS
AI-driven HDS is transforming pipeline monitoring through real-time fault detection, structural analysis, and environmental compliance. Beyond pipelines, applications extend to railways, bridges, and industrial infrastructure.
Clear benefits to operators and asset managers include:
- Highly precise monitoring
- Reduced downtime
- Lower operational risks
- Longer asset lifespans
- Overall improvements to bottom line
Connect with Hifi for Advanced Sensing Solutions
Hifi’s high-fidelity distributed sensing integrates AI and ML to deliver actionable insights for critical infrastructure. Contact us today to explore tailored solutions for your asset management needs.