Fiber Acoustic Monitoring with Audio Correlation for False Alarms
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
DAS/DVS infrastructures in fluid and gas pipelines generate a high number of false positives due to ambient disturbances, leading to inefficiency and high costs when calibrating sensitivity adjustments are ineffective.
Innovation Solution
A system integrating microphones, an audio classifier module, and a correlator module to analyze ambient noise using machine learning algorithms, distinguishing between recognized and unrecognized disturbances to reduce false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If DAS/DVS infrastructure is used for leak detection in pipelines, then real-time monitoring capability is improved, but false alarm rate increases due to ambient disturbances
Solution Approach 1:
The system segments the anomaly detection task into two independent modules: DAS/DVS for vibrational analysis and audio classification for acoustic analysis. Each module processes specific types of signals independently, then results are correlated to reduce false positives. This segmentation allows each module to specialize in detecting its specific signal type without interference from ambient disturbances that affect the other modality.
Solution Approach 2:
The correlator module acts as an intermediary that receives alarm signals from both DAS/DVS and audio classification modules, compares their results, and generates a final qualified alarm signal only when both modules agree. This intermediary mechanism filters out false positives from either individual module by requiring corroboration from the other sensing modality.
2Measurement precision
If sensitivity of DAS/DVS infrastructure is increased to detect more leaks, then detection capability is improved, but false positives increase due to ambient disturbances
Solution Approach 1:
The system changes the operational parameters of the two sensing modules independently, allowing each to be optimized for its specific detection task. The DAS/DVS module can maintain high sensitivity for vibrational anomalies while the audio classification module uses different acoustic parameters to filter ambient noise. The correlator then integrates these differently-parameterized detections to achieve both high sensitivity and low false alarm rate.
3Object-generated harmful factors
If calibration is performed to reduce false positives in DAS/DVS, then false alarm rate decreases, but operational flexibility is reduced and deactivation may be required
Solution Approach 1:
The system implements dynamic adaptability through the audio classification module, which can be continuously trained and updated with new ambient noise profiles specific to different operational environments. Unlike static calibration of DAS/DVS, the audio module can adapt to changing conditions over time, maintaining operational flexibility while reducing false alarms through environment-specific acoustic modeling.
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
Reduces false alarms by accurately identifying acoustic and vibrational anomalies, maintaining pipeline monitoring efficiency and reducing operational costs in areas with human interference.
Implementation Method 1
acoustic disturbances, as well as vibrations, generate microscopic strains and compressions of the optical fiber
Implementation Method 2
acoustic disturbances, as well as vibrations, generate microscopic strains and compressions of the optical fiber
Implementation Method 3
vibrations caused by a fluid coming out of the pipe through a leak modify the features of the reflected wave
Implementation Method 4
one or more microphones operatively associated with the site to be monitored and configured to sense one or more ambient audio signals associated with said at least one acoustic and/or vibrational anomaly
Data Source
AI summary
The disclosure relates to a system for sensing and recognizing acoustic and/or vibrational anomalies. The system comprises: a unit using optical fiber for the distributed acoustic and/or vibrational sensing of at least one acoustic and/or vibrational anomaly and for generating an alarm signal representative of the sensing of such at least one anomaly; one or more acoustic-electric transducers to sense one or more ambient audio signals associated with the at least one acoustic and/or vibrational anomaly and to convert such ambient audio signals into electrical signals; an audio classifier module configured to classify the electrical signals generated based on at least one algorithm for data analysis and machine learning of information from the data, to generate a classification signal; a correlator module configured to receive the alarm signal and the classification signal and to compare such signals to generate a qualified alarm signal.