Noise Source Identification Using Sensor Subarrays
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for analyzing noise sources in jet engines using arrays of microphones are limited by the size and location of the array, which restricts the collection of noise data to a limited number of emission angles, resulting in incomplete noise source identification and component breakdown.
Innovation Solution
An apparatus and method that utilize an array of sound sensors on a ground plane to select a subset of sensors based on ray tracing and curve intersections, forming a subarray to identify noise contributions from candidate sound source points, allowing for comprehensive noise source analysis across various angles and frequencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hundreds or thousands of array locations are used to cover all sound propagation paths, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent divides the large array of sound sensors into multiple subarrays, each responsible for specific spatial regions or frequency ranges. This segmentation allows comprehensive noise source identification through coordinated analysis of subarray data, reducing the complexity of managing individual sensor positions while maintaining measurement precision.
Solution Approach 2:
The patent uses a limited number of sound sensors in each subarray to capture sufficient noise propagation data for identification purposes. Rather than requiring complete coverage from all possible array locations, the method achieves effective noise source identification through strategically positioned subarrays, reducing overall device complexity.
2Device complexity
If microphones are placed at limited locations, then device complexity is reduced, but measurement precision deteriorates
Solution Approach 1:
The patent processes noise propagation data in the frequency domain by transforming time-domain signals into frequency spectra. This dimensional transformation allows limited physical sensor positions to provide comprehensive noise source identification by analyzing frequency characteristics across multiple subarrays, effectively compensating for reduced spatial coverage.
3Device complexity
If a limited number of microphones are used, then device complexity is reduced, but loss of information increases
Solution Approach 1:
The patent performs Fourier transformation on noise signals to convert them from time domain to frequency domain before analysis. This preliminary action extracts frequency spectrum information that contains noise source identification data, allowing complete information retrieval from limited sensor measurements by utilizing frequency domain characteristics.
Solution Approach 2:
The patent introduces frequency spectrum analysis as an intermediary processing step between raw microphone signals and noise source identification. This intermediary transformation enables comprehensive noise propagation information to be derived from limited sensor data by analyzing frequency characteristics and propagation patterns across multiple subarrays.
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 the identification of noise source components with improved spatial resolution and frequency coverage, overcoming the limitations of traditional array designs by selecting optimal subarrays for noise data analysis, thereby enhancing the accuracy of noise source identification and component breakdown.
Implementation Method 1
Noise data are received for the noise source from an array of sound sensors on a ground plane
Data Source
AI summary
A computer implemented method and apparatus for identifying component breakdown of noise sources. Noise data is received for a noise source from an array of sound sensors. Measurement points of interest, candidate sound source points along an axis, and array aperture angles are identified. Sets of first and second bounding traces are identified from ray traces extending from the candidate noise source points towards the measurement points of interest using the array aperture angles. The bounding ray traces are rotated around the axis to form sets of first and second surfaces. Sets of first and second curves are identified from an intersection of the sets of first and second surfaces with the ground plane. Sound sensors are selected from the array using the curves to form subarrays. The component breakdown of noise generated by the noise source is identified using noise data from sound sensors in the subarrays.