Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems
Abstract
:1. Introduction
2. Methods
2.1. Framework Development
2.1.1. Maintenance System Architecture
2.1.2. Implementation of the Proposed System Architecture
2.1.3. Training and Testing of ML Models
2.2. Data-Driven Predictive Models
3. Results
4. Discussion
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Tuning Method | Best Parameters |
---|---|---|
Linear Regression | Ridge Regression | {‘alpha’: 1, ‘fit_intercept’: True, ‘solver’: ‘auto’} |
XGB Regressor | Randomized Search | {‘colsample_bytree’: 0.78, ‘gamma’: 0.007, ‘learning_rate’: 0.29, ‘max_depth’: 8, ‘n_estimators’: 341, ‘subsample’: 0.75} |
Random Forest Regressor | Grid Search | {‘max_depth’: None, ‘max_features’: ‘log2’, ‘min_samples_leaf’: 1, ‘min_samples_split’: 5, ‘n_estimators’: 100} |
AdaBoost Regressor | Grid Search | {‘estimator__max_depth’: 3, ‘learning_rate’: 1.0, ‘loss’: ‘square’, ’n_estimators’: 50 |
SVR | Randomized Search | {‘C’: 1.03, ‘epsilon’: 0.105, ‘gamma’: ‘scale’, ‘kernel’: ‘rbf’ |
MLP | Randomized Search | {‘activation’: ‘tanh’, ‘alpha’: 0.00208, ‘batch_size’: 128, ‘hidden_layer_sizes’: (50, 50), ‘learning_rate_init’: 0.0619} |
Model | Tuning Method | Best Parameters |
---|---|---|
Linear Regression | Ridge Regression | {‘alpha’: 0.01, ‘fit_intercept’: True, ‘solver’: ‘svd’} |
XGB Regressor | Randomized Search | {learning_rate = 0.29, max_depth = 8, n_estimators = 341, subsample = 0.75, colsample_bytree = 0.78, gamma = 0.007} |
Random Forest Regressor | Grid Search | {‘estimator__max_depth’: 3, ‘learning_rate’: 1.0, ‘loss’: ‘square’, ‘n_estimators’: 200} |
AdaBoost Regressor | Grid Search | {‘estimator__max_depth’: 3, ‘learning_rate’: 1.0, ‘loss’: ‘square’, ‘n_estimators’: 50} |
SVR | Randomized Search | {‘C’: 98.96484985318548, ‘epsilon’: 0.03416347415306914, ‘gamma’: ‘auto’, ‘kernel’: ‘rbf’} |
MLP | Randomized Search | {‘activation’: ‘logistic’, ‘alpha’: 0.010151348978007608, ‘batch_size’: 32, ‘hidden_layer_sizes’: (50, 50), ‘learning_rate_init’: 0.015226341829186325} |
ML Algorithm | MAPE | RMSE | ||
---|---|---|---|---|
Linear Regression | 0.020 ± 0.002 | 26.5% ± 3.4% | 0.027 ± 0.003 | 86.0% ± 2.8% |
XGB Regressor | 0.020 ± 0.003 | 24.6% ± 4.8% | 0.027 ± 0.004 | 85.0% ± 5.3% |
Random Forest Regressor | 0.011 ± 0.002 | 15.6% ± 4.0% | 0.017 ± 0.003 | 94.2% ± 2.4% |
Average Ensemble | 0.016 ± 0.001 | 23.0% ± 3.8% | 0.018 ± 0.002 | 93.1% ± 2.1% |
AdaBoost Regressor | 0.059 ± 0.003 | 56.2% ± 4.6% | 0.067 ± 0.004 | 9.7% ± 18.9% |
SVR | 0.022 ± 0.005 | 29.4% ± 8.0% | 0.027 ± 0.006 | 84.5% ± 8.5% |
MLP | 0.018 ± 0.002 | 24.7% ± 4.1% | 0.023 ± 0.003 | 89.4% ± 2.0% |
Comparison | Z-Score | p-Value | Significant (p < 0.05) |
---|---|---|---|
Random Forest vs. Linear Regression | 12.0094 | 2.28606 × 10−17 | True |
Random Forest vs. XGB Regressor | 8.25095 | 2.32836 × 10−11 | True |
Random Forest vs. Average Ensemble | 8.20355 | 2.7945 × 10−11 | True |
Random Forest vs. AdaBoost | 1.95375 | 0.055559 | False |
Random Forest vs. SVR | 23.9245 | 9.76039 × 10−32 | True |
Random Forest vs. MLP | 5.9513 | 1.63957 × 10−7 | True |
ML Algorithm | MAPE | RMSE | |||
---|---|---|---|---|---|
Linear Regression | 64.076 ± 3.503 | 11.5% ± 1.2% | 87.650 ± 4.477 | 95.2% ± 0.4% | |
XGB Regressor | 22.513 ± 4.424 | 3.0% ± 0.5% | 42.650 ± 8.933 | 98.9% ± 0.5% | |
Random Forest Regressor | 34.959 ± 4.336 | 4.7% ± 0.6% | 52.650 ± 7.886 | 98.3% ± 0.5% | |
Average Ensemble | 49.369 ± 3.001 | 7.8% ± 0.5% | 58.650 ± 3.119 | 97.9% ± 0.3% | |
AdaBoost Regressor | 29.339 ± 4.861 | 3.8% ± 0.8% | 57.650 ± 11.564 | 97.9% ± 0.7% | |
SVR | 75.970 ± 12.434 | 6.3% ± 1.0% | 186.650 ± 21.157 | 78.5% ± 3.5% | |
MLP | 34.231 ± 4.613 | 3.8% ± 0.5% | 58.650 ± 8.667 | 97.8% ± 0.5% |
Comparison | Z-Score | p-Value | Significant (p < 0.05) |
---|---|---|---|
XGB Regressor vs. Linear Regression | 29.9667 | 5.46433 × 10−37 | True |
XGB Regressor vs. Random Forest | 4.6573 | 1.914 × 10−5 | True |
XGB Regressor vs. Average Ensemble | 8.20355 | 2.7945 × 10−11 | True |
XGB Regressor vs. AdaBoost | 7.78024 | 1.09453 × 10−13 | True |
XGB Regressor vs. SVR | 5.63292 | 5.44446 × 10−7 | True |
XGB Regressor vs. MLP | 31.2461 | 5.55743 × 10−38 | True |
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Khan, T.; Khan, U.; Khan, A.; Mollan, C.; Morkvenaite-Vilkonciene, I.; Pandey, V. Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems. Machines 2025, 13, 481. https://doi.org/10.3390/machines13060481
Khan T, Khan U, Khan A, Mollan C, Morkvenaite-Vilkonciene I, Pandey V. Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems. Machines. 2025; 13(6):481. https://doi.org/10.3390/machines13060481
Chicago/Turabian StyleKhan, Tarana, Urfi Khan, Adnan Khan, Calahan Mollan, Inga Morkvenaite-Vilkonciene, and Vijitashwa Pandey. 2025. "Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems" Machines 13, no. 6: 481. https://doi.org/10.3390/machines13060481
APA StyleKhan, T., Khan, U., Khan, A., Mollan, C., Morkvenaite-Vilkonciene, I., & Pandey, V. (2025). Data-Driven Digital Twin Framework for Predictive Maintenance of Smart Manufacturing Systems. Machines, 13(6), 481. https://doi.org/10.3390/machines13060481