Distributed Sensing Signal Energy Spectrum Analysis
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Solution Overview
Problem
Real-time energy spectrum analysis in distributed acoustic sensing systems is computationally challenging due to the immense amount of information collected, making it difficult to perform efficiently.
Innovation Solution
The proposed solution involves an energy spectrum analysis methodology that computes signal energy with low computational complexity, enabling real-time analysis by segmenting the distributed sensing signal into blocks and applying filters with lengths smaller than the block length, using an overlap-and-add filtering scheme and Fast Fourier Transform for efficient computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional energy spectrum analysis methods are applied to distributed sensing signals, then comprehensive frequency analysis can be obtained, but computational complexity becomes excessively high making real-time analysis infeasible
Solution Approach 1:
The distributed sensing signal is divided into multiple overlapping blocks, where each block is processed independently using filter banks. This segmentation allows the computational workload to be distributed across multiple smaller, parallel processing units, reducing the overall computational complexity while maintaining spectral analysis accuracy through the overlap region that captures transient frequency components.
2Loss of information
If the entire distributed sensing signal is processed simultaneously, then complete energy spectrum information is obtained, but processing time increases making real-time analysis difficult
Solution Approach 1:
The signal processing employs overlapping blocks with continuous processing, where adjacent blocks share common samples. This continuous action ensures that no spectral information is lost at block boundaries while enabling parallel processing of multiple blocks, thereby reducing overall processing time and achieving real-time energy spectrum analysis without compromising information completeness.
3Measurement precision
If filter length is increased to improve frequency resolution, then spectral analysis precision improves, but computational operations increase significantly
Solution Approach 1:
Instead of applying a single long filter to the entire signal, the method uses multiple shorter filters of length L applied to overlapping blocks of length M, where L < M. This partial action approach provides sufficient frequency resolution for each block while dramatically reducing the computational operations compared to a single long filter, with the overlap ensuring no spectral information is missed.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for real-time energy spectrum analysis, supporting various applications such as turbulent flow monitoring, plug leak detection, and wellbore integrity monitoring, by reducing the number of operations required and enabling efficient data processing.
Implementation Method 1
at least one fiber optic coupler that receives backscattered light and that produces optical interferometry signals from the backscattered light
Implementation Method 2
photo-detectors that produce an electrical signal for each of said optical interferometry signals
Data Source
AI summary
A system includes an optical fiber and an interrogator to provide source light to the optical fiber. The system also includes a receiver coupled to the optical fiber. The receiver includes at least one fiber optic coupler that receives backscattered light and that produces one or more optical interferometry signals from the backscattered light. The receiver also includes photo-detectors that produce an electrical signal for each of the one or more optical interferometry signals. The system also includes at least one digitizer that digitizes each electrical signal. The system also includes at least one processing unit that processes each digitized electrical signal to obtain a distributed sensing signal and related energy spectrum information. The energy spectrum information corresponds to energy calculated for each of a limited number of frequency subbands by segmenting the distributed sensing signal into blocks having a predetermined block length and by applying a filter having a filter length that is smaller than the predetermined block length.