الفهرس 
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Abstract
Predictive maintenance techniques are designed to determine the state of
equipment in action to help know when we can intervene to perform
maintenance on it. Predictive maintenance design applies artificial intelligence
techniques such as machine learning to analyze data and monitor efficiency.
In this thesis, we predict the maintenance of any equipment before it stops to
reduce unplanned equipment maintenance by means of machine learning
algorithms. We adopted in our work the following supervised machine
learning algorithms: Random Forest, Support Vector Machine, KNN,
Decision Tree, Logistic Regression, Naïve Bayes and XGBOOST.
Simulations and results show that the Random Forest and XGBOOST have
almost the same performance. However, results indicate that Random Forest
algorithm has value of F1-sore with 0.9973. Followed by the XGBOOST
algorithm has a F1-score relative value with 0.9884. The XGBOOST machine
learning algorithm is preferred for bigger dataset compared to Random Forest
and it works more effectively in the case of small dataset. In addition, we
adopted the anomaly detection by using Isolation Forest algorithm to detect
the anomaly data that used for predictive maintenance. Finally, the proposed
predictive maintenance system is successful in identifying possible failure
indicators and reducing some production stoppages as shown by our
simulation results.