Optical Fiber Sensing Solution for Pipeline Inspection
Algorithm-powered Distributed Optical Fiber Sensing Technology Enables Intelligent Pipeline Inspection
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Algorithm-powered Distributed Optical Fiber Sensing Technology Enables Intelligent Pipeline Inspection
Next-Generation All-Optical Sensing Safeguards Pipeline Security
In the oil and gas industry, pipeline inspection always relies on costly and inefficient manual inspection that is prone to safety risks and economic losses.Existing methods cannot sustain 24/7 real-time detection. In view of this, Huawei has released the Sensing OptiX solution that uses distributed fiber optic sensing (DFOS) technology. By using communication optical cables deployed alongside pipelines as sensors, the solution collects and analyzes vibration signals sent by optical fibers to accurately paint a picture of construction beyond pipelines.
Gain the Edge
Comprehensive detection, zero false negatives
Built-in super blind spot error correction algorithm,
improving the effective signal collection rate to 99.9%
Precise identification, 95% accuracy
32-dimensional vibration pattern analysis algorithm, improving accuracy to 95%
Fast learning, continuous evolution
Online learning, fast learning of local samples,
100-fold increase of learning efficiency in new scenarios
Architecture
The Optical Fiber Sensing Solution for Pipeline Inspection uses OptiXsense EF3000-A50. The device quickly analyzes vibration pattern information, identifies the event type, accurately locates the intrusion, and reports an alarm, implementing online real-time detection and security warning of the entire pipeline. The device uses an innovative enhanced oDSP with a built-in blind spot error correction algorithm that greatly enhances the effectiveness of weak signals and improves the effective signal collection rate to 99.9%. The device uses a vibration pattern analysis engine to analyze and restore construction events from multiple dimensions. For each vibration point, the vibration pattern analysis engine obtains a large amount of phase information, improving event identification accuracy to 95%. The device also uses an online learning algorithm to improve learning speed in new scenarios by 100 times.
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