出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：System and sub-system maintenance is a significant task for every dynamic system. A plethora of
approaches, both quantitative and qualitative, have been proposed to ensure the system safety
and to minimize the system downtime. The rapid progress of computing technologies and
different machine learning approaches makes it possible to integrate complex machine learning
techniques with maintenance strategies to predict system maintenance in advance. The present
work analyzes different methods of integrating an Artificial Neural Network (ANN) and ANN
with Principle Component Analysis (PCA) to model and predict compressor decay state
coefficient and turbine decay state coefficient of a Gas Turbine (GT) mounted on a frigate
characterized by a Combined Diesel-Electric and Gas (CODLAG) propulsion plant used in
naval vessels. The input parameters are GT parameters and the outputs are GT compressor and
turbine decay state coefficients. Due to the presence of a large number of inputs, more hidden
layers are required, and as a result a deep neural network is found appropriate. The simulation
results confirm that most of the proposed models accomplish the prediction of the decay state
coefficients of the gas turbine of the naval propulsion. The results show that a consistently
declining hidden layers size which is proportional to the input and to the output outperforms the
other neural network architectures. In addition, the results of ANN outperforms hybrid PCAANN
in most cases. The ANN architecture design might be relevant to other predictive
maintenance systems.
关键词：Condition based maintenance; Neural Network; Deep neural network; Principle Component;
Analysis(PCA); Naval propulsion
