Abstract
When rolling bearing fails, the vibration signal will show non-stationary characteristics. The characteristics of non-stationary vibration signal are closely related to the fault condition of the bearing. It is an effective way to diagnose the fault of the rolling bearing by analyzing the non-stationary vibration signal. In this paper, time-frequency analysis is used to decompose the vibration signal of the bearing, the matrix characteristics of the signal are obtained through singular value decomposition, and the fault diagnosis algorithm is constructed through the stacking method in machine learning. Finally, experiments have verified the effectiveness of the above theoretical methods in rolling bearing fault diagnosis.
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