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The health of bearings in mechanical equipment is crucial for production safety and stable operation. The vibration signal of a bearing contains abundant fault information, accurately reflecting its health condition. In this paper, the feature extraction algorithm for vibration signals is being studied and optimized for various bearing fault characteristics. In the time domain, statistical methods are applied to extract the peak value, root-mean-square (RMS), and other indicators reflecting the signal amplitude and energy. In the frequency domain analysis, Fast Fourier Transform (FFT) is used to find the characteristic frequency, and the operating quality of the bearing is determined through harmonic analysis[1]. In the time-frequency domain analysis, Wavelet Transform (WT) and Wigner-Ville distribution are utilized to extract the features of the non-stationary signals[2]. Combining these methods optimizes the feature selection logic further. Dimensionality reduction techniques, such as Principal Component Analysis (PCA), are utilized along with adaptive filtering and other methods for reducing noise in the original signal[3]. For the extracted features, a support vector machine (SVM)-based classifier design and optimization are conducted[4]. Simulation experiments are carried out using MATLAB to validate the effectiveness of the proposed algorithm for identifying fault types such as outer ring, inner ring, rolling body, and cage. The experimental results show that the algorithm exhibits high precision and recall in fault diagnosis, especially in complex noisy environments, while still maintaining good performance. This performance is superior to that of traditional methods. The optimization process and analysis method proposed in this study significantly enhance the accuracy of bearing fault diagnosis, offer reliable technical support for condition monitoring and maintenance of mechanical equipment, and hold significant application value for intelligent manufacturing and predictive maintenance.
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