The change of GFRF before and after the failure of the spring is extremely obvious. Therefore, by analyzing the change of GFRF, the fatigue failure of the spring can be effectively diagnosed. After the GFRF spectral feature data is compared with the sample spectral features in the standard spectral feature database, the pattern recognition technique can be used to determine the current working state of the spring. In this diagnostic method, there are three core links, namely GFRF identification, feature extraction and pattern recognition. GFRF identification is the basis for nonlinear spectral analysis. It uses the input data of the input of the system being diagnosed and uses the effective Volterra kernel identification algorithm to obtain the GFRF estimate of the system. Considering the need of online application and robustness, a fully solved Volterra kernel adaptive identification algorithm has been proposed for this purpose. For the shock absorbing spring, the acceleration signal of the upper and lower ends is sampled as the input signal, and the Volterra kernel identification and GFRFs online modeling of the damper spring can be performed online based on the adaptive algorithm. Then, the spectral feature extraction of the generalized frequency response function is performed to obtain the spectral feature information of the GFRFs of the system under test. After extracting the current GFRF spectral characteristic data from the damper spring, the simplest and most effective pattern recognition technique can be used to determine the current working state of the damper spring, such as using a neural network as a pattern recognition and classifier. During the working of the spring, the vibration displacement or vibration acceleration signals of the upper and lower ends are sampled online, and the observation data is used to establish the current estimation of the current order GFRF of the spring in real time. The current GFRF spectral signature of the damper spring is extracted from these estimated GFRFs.
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