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Kinetic Mechanical Engineering, 2022, 3(4); doi: 10.38007/KME.2022.030408.

Vibration Response Characteristics of Engineering Ship Power Machinery Based on Machine Learning


Vempaty Charu

Corresponding Author:
Vempaty Charu

University of Anbar, Iraq


In recent years, machine learning algorithms have flourished, and with superior performance, a new generation of information technology represented by machine learning algorithms has been widely used in the field of fault diagnosis. Researchers have proposed a large number of dynamics modelling and control algorithms based on machine learning, and these algorithms have achieved good results in the study of mechanical vibration signal response. Therefore, this paper takes the mechanical dynamics of engineering ships as the research object and adopts a machine learning-based approach to carry out research on mechanical fault diagnosis models. The paper focuses on the mechanical vibration signal fault model to capture the uncertainty of the mechanical dynamics system and the use of a model predictive control algorithm based on the machine learning model to determine the mechanical faults.


Machine Learning, Fault Diagnosis, Power Machinery, Vibration Signals

Cite This Paper

Vempaty Charu. Vibration Response Characteristics of Engineering Ship Power Machinery Based on Machine Learning. Kinetic Mechanical Engineering (2022), Vol. 3, Issue 4: 63-70. https://doi.org/10.38007/KME.2022.030408.


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