Vehicle Active Noise Cancellation Diagnostics Using Cross-Correlation
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Solution Overview
Problem
Existing active noise cancellation systems in vehicles face challenges in reliably diagnosing component degradation due to manufacturing tolerances and variations in cabin acoustics, leading to subjective and time-consuming assessments that can result in false positives or negatives.
Innovation Solution
A diagnostic tool using cross-correlation between a reference impulse response and the actual impulse response of the active noise cancellation system, generating correlation coefficients to assess system degradation, which can be performed rapidly and accurately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional time-based signal processing techniques are used to assess active noise cancellation systems, then the assessment process becomes subjective and time-consuming, but manufacturing tolerances and cabin acoustic variations make it difficult to establish reliable performance thresholds
Solution Approach 1:
The patent replaces traditional time-based signal processing techniques with cross-correlation analysis. This substitution transforms the diagnostic approach from subjective time-domain assessment to an objective frequency-domain method that compares impulse responses, eliminating subjectivity and enabling reliable threshold establishment despite manufacturing tolerances and acoustic variations.
Solution Approach 2:
The patent changes the diagnostic parameter from time-based signal processing to cross-correlation analysis in the frequency domain. By transforming the assessment methodology and comparing impulse responses through cross-correlation, the system establishes objective performance thresholds that are reliable across different vehicles despite manufacturing variations.
2Measurement precision
If subjective assessment methods are used to evaluate active noise cancellation systems, then diagnostic flexibility is maintained, but false positive and false negative indications increase
Solution Approach 1:
The patent replaces subjective human assessment with automated cross-correlation analysis. This objective method compares the measured impulse response against a reference, automatically determining whether degradation exists based on correlation coefficients, thereby eliminating false positives and false negatives associated with subjective judgment.
Solution Approach 2:
The patent implements a feedback mechanism where the cross-correlation result provides objective feedback on system degradation. The correlation coefficient serves as a quantitative metric that automatically indicates whether the active noise cancellation system is functioning within acceptable parameters, replacing subjective assessment with measurable feedback.
3Reliability
If comprehensive diagnostic testing is performed on active noise cancellation systems at the end of the assembly line, then system degradation can be detected, but the diagnostic process slows down vehicle production
Solution Approach 1:
The patent replaces comprehensive time-consuming diagnostic testing with rapid cross-correlation analysis. By using frequency-domain impulse response comparison instead of traditional time-based signal processing, the system achieves reliable degradation detection in a fraction of the time, maintaining assembly line productivity.
4Measurement precision
If multiple reference vehicles are used to establish performance thresholds, then diagnostic accuracy improves, but the complexity of establishing and maintaining reference data increases
Solution Approach 1:
The patent merges multiple reference impulse responses into a single averaged reference. By combining data from multiple reference vehicles through averaging, the system captures typical performance characteristics while simplifying the reference data structure, reducing management complexity while maintaining diagnostic accuracy.
Data Source
AI summary
Embodiments of a diagnostic tool for an active noise cancelling system are presented. The diagnostic tool may generate an output from the active noise cancelling system and capture input to the active noise cancelling system that results from the output of the active noise cancelling system. The diagnostic tool may generate visual output to aid in evaluation of active noise cancelling systems.


