|
Maintenance of
medical devices has become a top priority around the world, this
is due to the vital role of these devices in contributing to
saving human lives. Maintenance engineering makes use of data
analytics using statistical techniques to expose patterns to
capture relationships among the different variables that affect
the performance of medical devices. This research presents an
attempt to discuss an efficient approach of data analytics for
maintaining medical devices to enhance their reliability in
terms of the mean time between failure and availability. Data
analytics methodology is applied based on supervised machine
learning techniques by training the classification models to
improve reliability, availability. Raw data were collected from
the cooling system of the Magnetic Resonance Imaging MRI magnet
as a case study, then organized and cleaned to make it suitable
to build and train classification models. A new representation
of the data has been resampled using the Synthetic Minority
Oversampling Technique (SMOTE). Six different classification
models were trained and tested using Weka software, these are
Logistic regression, K-nearest neighbors, J48 and Random Forest
decision trees models, Support-vector machine SVM, and Naive
Bayes. Based on the F-measure, K-nearest neighbor, Random
Forest, and SVM were chosen to be the most significant
classifiers, a comparison between these three classifiers was
done based on evaluation metrics, the metrics are: Correctly
classified instances, Incorrectly classified instances, Root
mean squared error (RMSE), Recall, Precision, F-Measure, Area
under ROC curve, and Accuracy, it was found that the random
forest classifier has the best performance, highest accuracy,
and the least error.
Maintenance performance in terms of MTBF and availability are
estimated after applying the random forest model and compared
with their values before to determine the efficiency of the
classifier. It was found that the random forest model has caused
an improvement in MTBF by 879%, and an improvement in the device
availability by 11%. Data analytics plays a critical role in
improving efficiency and generating understandings from the data
generated by devices. Through this paper, data analytics was
followed for maintenance planning to improve the mean time
between failure and availability of the cooling system of the
magnetic resonance imaging device, the results were positive and
encouraging. The same methodology can be generalized to benefit
from it in maintenance planning for other medical purposes.
|