There has been an increasing demand on marine transportation
and traveling, since the voyage of the ships are more
economical and efficient than air or land based alternatives.
The propulsion of a ship is provided by a main engine system
which includes the shaft, the propellers, and other auxiliary
equipment. Marine diesel engine is a complex structure
that the faults within these machines can cause malfunction
of the whole system, which in turn inhibits the ship’s mission.
It is crucial to monitor the engine and other auxiliary
systems during the operation and infer their condition from
their diagnostic data. In this study, we analyze monitoring
data of a crude oil tanker for different ship loads and conditions.
Our primary analysis include main engine fault detection
and classification for which we propose an end-to-end
joint autoencoder-classifier model that contains a convolutional
autoencoder, and a long-short term memory regressor
connected to the the latent space. Genetic algorithms optimized
models gave us 93.61% accuracy for fault classification
task. Further investigation on feature’s contributions to
the model, we increased the accuracy upto 96%. One concern
about marine transportation is the pollution of the air
with green house effect gases. In this study, we have developed
NOx and SOx emission estimators for different faults
and working conditions. Leveraging ship load, working conditions
and engine faults in the models helped us to achieve
50% better estimation performance. Although there are other
studies regarding gases emissions in the literature, this is the
first study that took engine faults into account. We believe
that the joint autoencoder-classifier model will be useful for
other time series estimation task on other domains, especially
where the operating condition plays a role in the process.