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Research on Planning Management of Invalidation System for a Spring
The change in the Generalized Frequency Response Function (GFRF) before and after a spring failure is highly noticeable. This makes GFRF analysis an effective method for diagnosing fatigue failure in springs. By comparing the GFRF spectral feature data with the sample features stored in a standard spectral database, pattern recognition techniques can be applied to assess the current operational condition of the spring. This diagnostic approach involves three key stages: GFRF identification, feature extraction, and pattern recognition.
GFRF identification serves as the foundation for nonlinear spectral analysis. It utilizes the input data from the system being diagnosed and applies an efficient Volterra kernel identification algorithm to estimate the GFRF of the system. To meet the requirements of online application and robustness, a fully solved adaptive Volterra kernel identification algorithm has been developed. For shock-absorbing springs, acceleration signals from both the top and bottom ends are sampled as input. Using this adaptive algorithm, real-time Volterra kernel identification and GFRF modeling of the damper spring can be performed.
Once the GFRF model is established, spectral feature extraction is carried out to obtain the frequency characteristics of the GFRF under different conditions. After extracting the current GFRF spectral features from the spring, a simple and effective pattern recognition technique—such as a neural network—can be used to determine the spring’s working state. During operation, vibration displacement or acceleration signals from the upper and lower ends of the spring are continuously sampled. These observations are then used to update the current order GFRF estimation in real time. From these estimated GFRFs, the current GFRF spectral signature of the damper spring is extracted, providing valuable insights into its health status and potential failure risks.