KEYWORDS: Time-frequency analysis, Feature extraction, Education and training, Vibration, Wavelets, Signal processing, Wavelet transforms, Diagnostics, Data modeling, Process modeling
The article proposes a method for bearing fault diagnosis based on the waveform FFT spectrum of the dual-stream CNN. The method first uses FFT and wavelet transform to obtain the one-dimensional FFT spectrum and two-dimensional time-frequency map of bearing vibration, and then inputs them into the 1D-CNN channel and 2D-CNN channel of the model for feature extraction. After the feature information is fused and processed, the fault diagnosis is finally completed in the classification layer. The experimental results show that this model significantly improves the accuracy of bearing fault diagnosis.
Regarding the association between maternal mental and physical health and infant behavior, this paper proposes a method that integrates Grey Wolf Optimization (GWO) with Extreme Learning Machine (ELM) for the correlation and classification of infant behavioral features and maternal health indicators. Firstly, a dataset of maternal mental and physical health and infant behavioral features is established. Using the Random Forest method, physical and psychological indicators of the mother and their eight primary influencing factors are extracted from the dataset. Subsequently, the classification accuracy of infant behavior is used as the fitness function for the Grey Wolf algorithm. The GWO algorithm optimizes the kernel and regularization parameters of the ELM, which are then fed into the ELM to train the four key feature parameters of infant behavior. Finally, it is applied to the classification task of infant behavior. The results indicate that the classification method combining GWO and ELM has an accuracy of 86% and a precision of 91%. This outperforms traditional classification models such as XGBoost and GBDT in detection and classification. It also surpasses the performance of ELM optimized by traditional optimization algorithms like SSA and PSO, validating the feasibility and effectiveness of the proposed method.
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