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This invention belongs to the field of bearing fault diagnosis and provides a bearing fault detection method and system based on deterministic learning and convolutional neural networks. The method includes acquiring a one-dimensional bearing vibration signal, expanding it into a two-dimensional signal using a high-gain observer, standardizing the two-dimensional signal to obtain a normalized signal, modeling the internal dynamics of the bearing vibration signal based on a deterministic learning mechanism, generating a dynamic information graph of the bearing vibration signal, and performing fault detection using a pre-trained convolutional neural network model based on the dynamic information graph to obtain the bearing fault detection result. This invention, for the first time from a dynamic system perspective, visualizes the dynamics of bearing vibration signals as a dynamic information graph and detects fault occurrence through changes in the dynamics.