The traditional electromechanical fault diagnosis mode of the ship lock adopts the wired connection control intelligent control system, which cannot be remotely and wirelessly controlled through the control field and more strict control mode. The design of ship lock electromechanical remote fault diagnosis mode based on machine learning is proposed. The concept of machine learning and the characteristics of neural network are summarized. The fault diagnosis mode and fault diagnosis technology of electrical communication system are proposed. The experimental comparison of fault diagnosis accuracy of CNN SVM GA-SVM model shows that the deep learning bearing fault diagnosis model based on CNN network model has better performance.

Yang Yang. Ship Lock Electromagnetically Remote Fault Diagnosis Mode Based on Machine Learning. Machine Learning Theory and Practice (2022), Vol. 3, Issue 1: 18-26. https://doi.org/10.38007/ML.2022.030103.

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