Fiber Optic Cable Ice Thickness Estimation Using Hybrid DAS Signals
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Solution Overview
Problem
Existing methods for estimating ice thickness on fiber optic cables are inaccurate, fail to account for non-uniform ice accretion, and lack real-time monitoring capabilities, leading to potential cable damage and service disruptions.
Innovation Solution
A hybrid signal processing technique combining Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification (SSI) is applied to Distributed Acoustic Sensing (DAS) data for continuous, real-time ice thickness estimation, enhancing accuracy and adaptability across various cable types and environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional ice thickness estimation methods are used, then the monitoring system is simple, but the measurement precision is insufficient and real-time capability is lacking
Solution Approach 1:
The patent combines Frequency Domain Decomposition (FDD) and Stochastic Subspace Identification (SSI) into a hybrid signal processing framework. FDD extracts dominant frequencies from vibration signals while SSI identifies modal parameters and handles non-linear dynamics. This merging of two complementary methods achieves superior ice thickness estimation accuracy compared to using either method alone, while the systematic integration approach manages computational complexity through structured processing stages.
Solution Approach 2:
The signal processing is divided into distinct sequential stages: (1) raw DAS data acquisition, (2) preprocessing and filtering, (3) FDD for frequency extraction, (4) SSI for modal parameter identification, and (5) ice thickness calculation. This segmentation allows each stage to be optimized independently and facilitates real-time processing by breaking down the complex analysis into manageable computational blocks that can be executed continuously.
2Reliability
If real-time continuous monitoring is implemented, then the reliability of ice detection is improved, but the use of energy and computational resources increases
Solution Approach 1:
The system implements continuous real-time monitoring where DAS data is continuously acquired and processed through the hybrid FDD-SSI framework. The processing pipeline operates continuously without interruption, enabling immediate detection of ice formation and dynamic assessment of ice load. This continuous operation ensures high reliability in ice detection while the efficiency of the signal processing algorithms optimizes energy consumption.
Solution Approach 2:
The system incorporates feedback mechanisms where the ice thickness estimation results are continuously fed back to update the monitoring state and trigger appropriate responses. The modal parameters and frequency data from SSI and FDD are used to dynamically adjust monitoring intensity and alert thresholds, creating an adaptive feedback loop that maintains high reliability while optimizing computational resource allocation based on actual ice conditions.
3Measurement precision
If hybrid FDD-SSI signal processing is applied, then the measurement precision and noise robustness are improved, but the device complexity and processing time increase
Solution Approach 1:
The system performs preliminary actions by pre-processing the DAS data before it enters the hybrid FDD-SSI analysis. This includes filtering, denoising, and initial feature extraction that prepares the data for more complex analysis. By completing these preliminary steps in advance or as part of the continuous stream processing, the system reduces the computational burden during critical analysis phases, thereby minimizing overall processing time while maintaining high precision through the complete FDD-SSI pipeline.
Data Source
AI summary
Disclosed are systems and methods that employ distributed fiber optic sensing (DFOS)/distributed acoustic sensing (DAS) to monitor and provide real-time estimation of ice thickness on fiber optic communications facilities, and which integrate DSA data with a hybrid processing technique that combines frequency domain decomposition (FDD) and stochastic subspace identification (SSI). Aspects of our innovative systems and methods include: i) Hybrid Signal Processing Techniques; ii) Real-time, Continuous Ice Monitoring; iii) Enhanced Noise Robustness and Non-linear Dynamics Handling; and iv) Adaptability and Scalability.