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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 failures in springs. Once the GFRF spectral feature data is compared with the standard spectral features stored in a database, pattern recognition techniques can be applied to determine the current operational condition of the spring. This diagnostic approach consists of three main stages: GFRF identification, feature extraction, and pattern recognition.
GFRF identification serves as the foundation for nonlinear frequency analysis. It involves using input data from the system under diagnosis and applying an efficient Volterra kernel identification algorithm to estimate the GFRF of the system. To meet the requirements of real-time applications and robustness, a fully solved Volterra kernel adaptive identification algorithm has been developed. For shock-absorbing springs, acceleration signals from both the top and bottom ends are sampled as input signals. Using this adaptive algorithm, it is possible to perform online Volterra kernel identification and GFRF modeling of the damper spring.
Once the GFRF model is established, spectral feature extraction is carried out to obtain the frequency response characteristics of the system. After extracting the current GFRF spectral features from the damper spring, the most straightforward and effective pattern recognition technique—such as a neural network—can be used to classify the spring’s working condition. During operation, vibration displacement or acceleration signals from the top and bottom ends of the spring are continuously sampled. These observations are then used to build a real-time estimation of the current order GFRF of the spring. From these estimated GFRFs, the current GFRF spectral signature of the damper spring is extracted, providing valuable insights into its health status.