Nonlinear sparsity-based instantaneous dynamic frequency fault diagnosis method for aviation intermediate bearing
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Solution Overview
Problem
Traditional fault diagnosis methods for intermediate bearings in aeroengines face challenges due to variable contact angles, complex fault features, and weak fault feature transmission, making it difficult to accurately diagnose faults, especially in the early stages where fault features are masked by strong background noise.
Innovation Solution
A nonlinear sparsity-based instantaneous dynamic frequency fault diagnosis method that enhances weak fault features through a nonlinear sparse time-frequency enhancement model, utilizing the coupling phenomenon of high and low voltage rotating frequencies, and reduces noise interference to improve feature extraction and diagnosis accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional spectral analysis and time-frequency analysis methods are used, then the analysis is simple and fast, but the weak fault features remain weak and are difficult to extract from strong background noise
Solution Approach 1:
The patent applies nonlinear compression transform to change the amplitude parameter relationship between fault features and background noise. By using a derivative window function in the time-frequency domain, the transform nonlinearly compresses the amplitude distribution, causing weak fault features to be enhanced relative to strong background noise components. This parameter transformation enables effective fault feature extraction from noisy signals where traditional linear methods fail.
2Measurement precision
If nonlinear compression transform is used to enhance weak fault features, then the feature representation is improved, but noise components are also enhanced and noise robustness is reduced
Solution Approach 1:
The patent applies local quality by using a derivative window function that provides different compression ratios for different amplitude levels and frequency components. The transform selectively enhances weak fault features while applying different processing characteristics to noise components based on their local time-frequency characteristics. This localized differential enhancement maintains noise robustness while improving fault feature representation.
3Device complexity
If traditional fault feature frequency extraction is used, then the method is simple, but it cannot handle variable contact angles and complex fault features of intermediate bearings
Solution Approach 1:
The patent transitions from traditional single-frequency fault feature extraction to a two-dimensional time-frequency analysis approach. By applying nonlinear compression transform in the time-frequency domain, the method can capture instantaneous dynamic frequency characteristics and variable contact angle effects that single-frequency methods miss. This dimensional expansion enables accurate diagnosis of complex intermediate bearing faults while maintaining reasonable methodological complexity.
Data Source
AI summary
The present disclosure discloses a nonlinear sparsity-based instantaneous dynamic frequency fault diagnosis method for an aviation intermediate bearing. In the method, a vibration signal and a rotating speed signal of high and low voltages of the intermediate bearing are acquired, and a vibration signal fragment x under a specific working condition is intercepted according to the rotating speed signal; a nonlinear sparse time-frequency enhancement model or a nonlinear sparse enhancement algorithm model is established based on derivative window function short-time Fourier transform of the vibration signal; the nonlinear sparse time-frequency enhancement model is solved or the nonlinear sparse enhancement algorithm model is improved by using a fast iterative shrinkage threshold algorithm and combining a k sparse strategy, and finally a nonlinear sparse time-frequency representation result {circumflex over (N)}x, may be obtained through iterative optimization.