Research about Predictive Maintenance using Machine learning

1. Machine Learning Approaches for Multiclass
Predictive Maintenance of Industrial Rotating
Machinery
Murad Abasov • Nourhane Riham Ouahdi Yohan Arenas • Waleed Ali
ELTE, Eötvös Loránd University
Faculty of Informatics
Savaria Institute of Technology, Szombathely

2. Research Context
• The Maintenance Challenge
• Maintenance represents up to 60% of production costs.
• Ineffective maintenance causes $60 billion USD loss annually (US, 2002)
Reduces product quality and plant profitability.
• Industry 4.0 Solution
• PdM can reduce unplanned downtime by up to 50%.
• Extends equipment life cycles by more than 30%.
• Leverages IoT, Big Data, and AI methods.
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3. Types of Maintenance Strategies 2

4. Knowledge Gaps in Current PdM
Research
1.
Limited Failure Classification
• Most studies focus on binary fault detection (failure vs. no-failure), leaving multiclass
and multi-label failure prediction underexplored.
2.
Standardized Data Fusion Frameworks
• Few standardized frameworks for integrating heterogeneous sensor modalities.
3.
Explainability Integration
• Limited studies on how feature attribution can improve maintenance decision-making.
4.
Data Scarcity & Imbalance
• Rare fault types hinder model generalization.
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5. Compare ML Algorithms in a multiclass predictive maintenance framework for industrial rotating machinery.
Compare ML Models
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Detect Multiple Failures
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Feature Analysis
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Bridge the gap
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MAIN GOAL

6. METHODOLOGY
• Data Collection:
AI4I Predictive Maintenance Dataset from Kaggle (10,000
rows × 14 features).
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7. DATASET FEATURES
Key features:
• Product ID
• Air Temperature (K)
• Process Temperature (K)
• Rotational Speed (rpm)
• Torque (Nm)
• Tool Wear (min)
• Failure types:
Failure modes:
• Tool Wear Failure (TWF)
• Heat Dissipation Failure (HDF)
• Power Failure (PWF)
• Overstrain Failure (OSF)
• Random Failure (RNF)
Note: Machine fails if at least one
failure mode is true.
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8. DATA PROCESSING STEPS
1.Data Cleaning
• Missing or inconsistent records checked and removed.
2.Normalization
• Min-Max scaling applied to continuous variables
(temperature, speed, torque, tool wear).
3.Encoding
• One-hot encoding for categorical variables (Product ID).
4.Data Splitting
• 80% training / 20% testing.
5.Handling Imbalance
• SMOTE (Synthetic Minority Over-sampling Technique)
applied to balance class distribution.
Validation Strategy
5-fold cross-validation to ensure
generalization and robustness
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9. MODEL PERFORMANCE
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10. RESULTS
Model Accuracy Precision Recall F1-score
Random Forest 0.961 0.955 0.948 0.951
SVM 0.924 0.918 0.905 0.911
Logistic Regression 0.876 0.869 0.854 0.861
• Random Forest showed the highest accuracy due to its ability to handle nonlinear and multiclass data.
• SVM performed well but was affected by class imbalance.
• Logistic Regression struggled with complex relationships.
• The study extends beyond binary prediction by implementing multiclass classification.
• Ensemble models like RF proved robust and reliable for predictive maintenance tasks.
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11. IMPACT
For Industry
• Reliable baseline for real-time
PdM.
• Applicable to CNC machines,
turbines and pumps.
• Reduces maintenance costs.
• Improves equipment reliability
For Decision-Making
• Multiple failure modes
detected simultaneously.
• Better maintenance
scheduling.
• Enhanced operational safety.
• Data-driven insights.
Trade-offs to Consider
Balance between accuracy, interpretability, and computational cost
when deploying on industrial hardware.
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12. LIMITATIONS
1.Synthetic Dataset
• May not fully capture stochastic variability, sensor noise, or missing
data from real industrial environments.
2.Limited to Classical ML
• Deep learning, hybrid models, and transformer architectures were
not explored.
3.No Temporal Analysis
• Time-series features and Remaining Useful Life (RUL) estimation
not incorporated.
4.Feature Engineering
• Advanced time-frequency decomposition techniques not applied.
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13. Future work :
•Applying the model to real industrial data.
•Incorporating time-series feature extraction.
•Evaluating the performance of deep learning
and hybrid methods.
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14. CONCLUSION
Why RF Succeeds
• Handles heterogeneous
sensor data.
• Robust to noise and outliers
• Reduces overfitting via
bagging
• Captures non-linear patterns
Other Models
• SVM: Sensitive to parameter
tuning and class imbalance.
• LR: Limited for nonlinear
relationships.
Feature Importance Insights
Torque, rotational speed, and tool wear identified as most influential factors
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