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October 1, 2025Applied Computer ScienceOpen Access

Prediction of remaining useful life and downtime of induction motors with supervised machine learning

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Authors

MAMuhammad Dzulfiqar ANINDHITOSSSuharjito Suharjito

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Overview

This research demonstrates supervised learning techniques on vibration data, indicating high accuracy in downtime classification.

Key Points

  • The decision tree and naive bayes models achieved 100% accuracy in classifying downtime, showcasing their effectiveness.
  • For predicting remaining useful life (RUL), the random forest model outperformed others, achieving lower mean absolute error.
  • A data preprocessing stage ensured data integrity by handling missing data and removing duplicates before analysis.
  • Supervised learning algorithms like decision tree, random forest, and artificial neural network were utilized to enhance prediction accuracy.

Cite This Study

ANINDHITO et al. (2025) studied this question.

synapsesocial.com/papers/68dd91cffe798ba2fc498b07https://doi.org/10.35784/acs_7299
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