Intelligent early structural health prognosis with nonlinear system identification for RFID signal analysis

The mechanical state of non-linear processes includes these characteristics such as multivariable, strong coupling, multiple vibration sources, large signal noise, and various random factors. The intrinsic relationship and overall consistency optimization of the characteristic factor direction paths of multi-source matrix signals are a new research hotspot in multi-source dynamic characteristic signal identification. A intelligent fault diagnosis and prognosis method based on NARMAX-FRF and PCA is proposed in this paper. This method can be widely used in industrial system fault diagnosis. This system solves many basic key problems, including identifying a non-linear model from a detected system, accurately solving the frequency response function, extracting a representative frequency domain from the frequency response function, and applying extracted system frequency domain features for large-scale structural health assessment. In order to verify the performance of the NARMAX_FRF and PCA method for nonlinear defect signal analysis, the experiment of intelligent RFID system of corrosion monitoring and the TOFD experimental system are analyzed for the structural health monitoring in this paper. The set of samples in the experiment of intelligent RFID system of corrosion monitoring consists of coated and uncoated mild steel plates, which have a patch that has been exposed to the environment for different durations to create different levels of corrosion. The results show the effectiveness and robustness of the proposed method.