Water hammer detection based on FIV online analysis using a distributed fiber optic sensor

Area of Science:

• Pipeline Engineering
• Structural Health Monitoring
• Fiber Optic Sensing Technology

Background:

• Continuous water hammer effects in long-distance oil pipelines pose significant risks, including damage, leakage, and collapse.
• Existing monitoring methods may lack the real-time, non-invasive, or long-distance capabilities required for effective detection.
• Abnormal fluid flow states can induce damaging vibrations within pipeline infrastructure.

Purpose of the Study:

• To introduce a novel detection method for continuous water hammer effects in long-distance pipelines.
• To utilize fiber optic sensing and a phase-sensitive optical time-domain reflectometry (φ-OTDR) system for non-invasive monitoring.
• To assess the intensity of flow-induced vibration (FIV) by distinguishing water hammer signals from normal vibrations.

Main Methods:

• Employed a non-invasive, low-cost, real-time monitoring approach using a sensing optical fiber and φ-OTDR system.
• Monitored pipeline wall deformation characteristics to identify abnormal flow states.
• Calculated the peak-to-average ratio (PAR) of vibration signals to statistically distinguish water hammer events from normal operational vibrations.

Main Results:

• Effectively monitored the FIV status of various pipeline types by analyzing the PAR of water hammer and vibration signals.
• Demonstrated high consistency between measured PAR values from the φ-OTDR system and simulation results.
• The distributed fiber optic sensor exhibited superior detection distance and anti-interference capabilities compared to commercial acceleration sensors.

Conclusions:

• The proposed φ-OTDR-based method provides an effective solution for detecting continuous water hammer in long-distance pipelines.
• The PAR analysis of vibration signals is a reliable indicator of FIV intensity and pipeline integrity.
• Distributed fiber optic sensing offers a robust and advantageous alternative for pipeline monitoring in challenging environments.