Table
5. Euclidean distances
Conclusions
We have proposed using HS to develop a hierarchical model for
forecasting failure rates. This approach shines especially when the data
have unbalanced category and structured characteristic. We demonstrated
the applicability of the model using a real-world dataset of failure
rate data from Naval ships and compared it with previous methods.
Through these comparisons, we confirmed that the prediction performance
of our novel model in the given dataset was greatly improved. Moreover,
we have shown how qualitative knowledge, such as the belonging to the
same series or construction era, could be incorporated into the model;
this approach was justified by further analyzing the relationship
between each parameter. These techniques could greatly improve Naval
ship management efficiency.
Some improvement could be noted for further studies. First, prevention
repair which may affect the failure pattern could be considered. A more
advanced model that incorporates the probability of failure after the
prevention repair is needed to design a model. Second is convergence and
evaluation measures. There were few instances with low E-BMI and
effective sample size, n_eff. Improving the model in terms of higher
E-BMI and n_eff measures would result in a better fit of the model.
Thirdly, due to substantial operational differences between combat and
non-combat ships, only combat ships are used in this paper. However, if
the differences could be incorporated in the further models, by using
categorical variables, a more accurate model could be possible based on
a larger amount of data.
The proposed model can contribute greatly to the following areas. First,
failure rate prediction could be used as a quantitative reference when
establishing a maintenance policy. Proper maintenance not only improves
the availability and mission completion rates but also reduces the
budget by reducing unnecessary maintenance. Second, from a broader
perspective, the predicted failure trend can be a qualitative reference
for designing the optimal life cycle of a ship. For instance, based on
our results, the failure rate increases dramatically as the ship becomes
senile. Therefore optimal retirement period could be decided by
balancing the maintenance and construction cost.
The proposed model can contribute greatly to the following areas. First,
failure rate prediction could be used as a quantitative reference when
establishing a maintenance policy. Proper maintenance not only improves
the availability and mission completion rates but also reduces the
budget by reducing unnecessary maintenance. Second, from a broader
perspective, the predicted failure trend can be a qualitative reference
for designing the optimal life cycle of a ship. For instance, based on
our results, the failure rate increases dramatically as the ship becomes
senile. Therefore optimal retirement period could be decided by
balancing the maintenance and construction cost.
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