Fault prediction of marine auxiliary boiler based on 1DCNN-LSTM

Deep Learning Technology is introduced into fault prediction algorithm of marine auxiliary boiler. The long short-term memory（LSTM） neural network is used to build the prediction model. The source data is processed by 1D Convolutional Neural Network to extract features and do preliminary classification. This process is to overcome the limitation of LSTM model in types of learning data feature and the dependence on the sequence of time series data. Both real data from Alfa Laval OS TCI marine oil fuel auxiliary boiler on board and simulating data set generated by DMS-CSS marine engine simulator are processed by the model for verification. Meantime a LSTM model does the same job for comparison. Experiments show that the mean square error and root-mean-square error of the output from 1DCNN-LSTM model are reduced by 0.0075 and 0.033 respectively compared to those from LSTM model.