The monitoring cloud platform of the ship power system can provide an important guarantee for the autonomous navigation of intelligent ships, and it can process, store and analyze the data generated in the navigation process. The change of its running state will affect the effective evaluation of intelligent equipment, navigation environment and driving behavior of ships by technicians. This paper mainly studies the design of engineering ship power machinery serialized monitoring system integrated with deep learning. This paper mainly uses NI data acquisition equipment to collect multi-dimensional vibration signals of shafting, gear box and bearing, and uses vibration method to monitor and diagnose the running state of shafting, and develops a ship shafting condition monitoring and fault diagnosis system based on LabVIEW. In order to improve the efficiency and accuracy of ship shafting fault identification and diagnosis, a ship shafting fault diagnosis method based on deep learning was proposed. The deep belief network (DBN) method is applied to shafting fault diagnosis.

Mateev Rafael. Engineering Ship Power Machinery Serialized Monitoring System Integrated with Deep Learning. Kinetic Mechanical Engineering (2021), Vol. 2, Issue 1: 29-37. https://doi.org/10.38007/KME.2021.020104.

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