Defect Detection in Devices Under Test Amid Ambient Noise
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Solution Overview
Problem
Existing defect detection techniques for devices under test fail when ambient noise is present and exceeds the linear signal, leading to unreliable measurements, especially when the noise source is unknown or moving, and current methods require extensive hardware and increased measurement time.
Innovation Solution
A diagnostic system using spatial and signal analysis to separate defect and noise sources, employing a source analyzer that generates defect and noise vectors, and a classificator to identify and localize defects, even with minimal hardware, and a novel demodulation technique to improve signal-to-noise ratio and reduce measurement time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional defect detection techniques are used, then measurement reliability is maintained in low noise conditions, but the system fails when ambient noise becomes dominant
Solution Approach 1:
The patent segments the measured signal into distinct components: linear signal, regular distortion, irregular deterministic distortion, stochastic distortion, and ambient noise. This segmentation allows each component to be processed and analyzed separately, enabling reliable defect detection even when ambient noise is dominant by focusing on the characteristic features of defect-related signals rather than the overall noisy signal.
Solution Approach 2:
The patent introduces an intermediary processing stage that uses cross-spectral density analysis and coherence functions as mediators between the raw noisy signal and defect detection. These intermediaries filter out ambient noise by exploiting the fact that defect-related signals are coherent with the excitation signal while ambient noise is not, thus maintaining reliability in high noise conditions.
2Measurement precision
If ambient noise is reduced by repeating measurements, then measurement accuracy improves, but measurement time increases significantly
Solution Approach 1:
The patent changes the parameter domain from time-domain averaging to frequency-domain analysis using cross-spectral density and coherence functions. By transforming the analysis to the frequency domain, the system can achieve noise immunity through parameter transformations rather than time-consuming repetitions, maintaining measurement precision while dramatically reducing measurement time to fit within production cycle times.
3Measurement precision
If a second noise microphone is placed closer to the ambient noise source, then noise measurement accuracy improves, but the system requires known noise source position and specific geometric constraints
Solution Approach 1:
The patent makes the single measurement microphone universal by using it for both defect signal measurement and ambient noise characterization. The system processes the measured signal to separate defect-related components from noise components through spectral analysis, eliminating the need for a dedicated noise microphone and the geometric constraints it would require. This multi-functional approach works with unknown or moving noise sources.
Solution Approach 2:
The patent extracts the ambient noise characteristics from the combined measured signal through cross-spectral density analysis. By computing the cross-spectral density between the excitation signal and the measured signal, and analyzing coherence, the system separates and extracts noise components without requiring a separate noise measurement path or knowledge of noise source position.
4Measurement precision
If more sensors are added to improve spatial analysis, then source separation accuracy improves, but hardware cost and system complexity increase
Solution Approach 1:
The patent replaces the mechanical approach of using multiple physical sensors with a signal processing approach using cross-spectral density analysis and coherence functions. This substitution achieves source separation accuracy through mathematical processing of signals from a single microphone, eliminating the need for additional sensors while maintaining or improving separation accuracy through sophisticated frequency-domain analysis.
Data Source
AI summary
An arrangement and method for assessing and diagnosing the operating state of a device under test in the presence of a disturbing ambient noise and for detecting, localizing and classifying defects of the device which affect its operational reliability and quality. At least two sensors monitor signals at arbitrary locations which are affected by signals emitted by defects and by ambient noise sources. A source analyzer receives the monitored signals, identifies the number and location of the sources, separates defect and noise sources, and analyzes the deterministic and stochastic signal components emitted by each source. Defect and noise vectors at the outputs of the source analyzer are supplied to a defect classificator which detects invalid parts of the measurements corrupted by ambient noise, accumulates the valid parts, assesses the quality of the system under test and identifies the physical causes and location of the defects.