Pressurized Fluid Duct Failure Characterization with Acoustic Localization
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Solution Overview
Problem
Existing methods for detecting failures in pressurized fluid ducts, such as leaks or bursts, are inadequate as they often fail to detect minor leaks and can be slow due to reliance on temperature and pressure sensors, especially when sensors are far from the failure location, leading to potential engine damage.
Innovation Solution
A method using a plurality of microphones to monitor acoustic signals, determine failure thresholds, estimate detection times, and apply a time-domain beamforming algorithm to localize and characterize failure events in pressurized fluid ducts, incorporating spectral analysis to assess severity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If temperature and pressure sensors are used to monitor duct failures, then the system can detect major failures, but minor leaks cannot be detected and detection time is significant
Solution Approach 1:
The patent replaces traditional temperature and pressure sensors with an acoustic monitoring system using microphones and signal processing. This substitution enables detection of minor leaks through acoustic signatures that precede pressure changes, reducing detection time while improving reliability across the full range of failure severities
Solution Approach 2:
The system performs preliminary detection of failures at their earliest stages by monitoring acoustic signals before they propagate into pressure changes. By detecting the acoustic signature of a leak immediately when it occurs, the system enables early intervention before damage escalates
2Ease of operation
If sensors are positioned far from the duct, then installation is easier, but detection time increases and accuracy decreases
Solution Approach 1:
The patent uses acoustic waves as intermediaries to transmit failure information from the duct to remote sensors. Microphones positioned away from the duct can still detect failures accurately because acoustic signals propagate through the fluid medium, enabling both easy installation and rapid detection
3Device complexity
If traditional monitoring methods are used, then the system structure is simple, but the precision of failure characterization is insufficient
Solution Approach 1:
The system dynamically adapts its monitoring and analysis capabilities based on detected acoustic patterns. By using real-time signal processing, spectral analysis, and adaptive thresholding, the system achieves high precision in failure characterization while maintaining operational simplicity through automated decision-making algorithms
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
Enables rapid and accurate localization and characterization of failure events, reducing the risk of engine damage by quickly identifying and assessing the nature and severity of duct failures.
Implementation Method 1
monitoring a plurality of acoustic signals, each acoustic signal of the plurality of acoustic signals corresponding to a recording of sound waves by a respective microphone of a plurality of microphones
Data Source
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AI summary
The present disclosure provides a method of characterising a failure event in a pressurised fluid duct, comprising: monitoring a plurality of acoustic signals, each acoustic signal of the plurality of acoustic signals corresponding to a recording of sound waves by a respective microphone in an environment of the duct, wherein each microphone is located at a predetermined position relative to the duct; detecting that a failure event in the duct has occurred based on an acoustic signal of the plurality of acoustic signals exceeding a first failure threshold, and identifying a failure marker as a time at which the failure event is detected; estimating, for each acoustic signal of the plurality of acoustic signals, a failure detection time, based on the failure marker and a rate of change in sound pressure level for each acoustic signal; determining an acoustic weighted position for the plurality of acoustic sensors based on the respective failure detection time for each acoustic signal and the position of each microphone; determining an estimate of a region containing the failure event as a first sub-volume of the duct based on the acoustic weighted position and a predetermined relationship between sound pressure level and regions of the duct; calculating, for each acoustic signal of the plurality of acoustic signals, one or more spectral characteristics; and determining at least one failure characteristic of the failure event based on a comparison between the one or more spectral characteristics, the estimate of the region containing the failure event, and one or more predefined failure event parameters.