Abstract
Sensor-fusion and different machine-learning methods were used for tool condition monitoring (TCM) when sawing wood in harsh conditions using power, sound, vibration, and acoustic emission (AE) signals. Tool classification was performed using two ensemble learning (XGBoost and random forest) methods and SVM. It was discussed that the optimal combination of sensors for monitoring is a trade-off between the accuracy of classifiers and the tolerance for sensor redundancy. AE was shown to be the critical sensor, which combined with power signals and XGBoost resulted in ∼92% classification accuracy. Ensemble learning outperformed the SVM and showed superior performance for TCM using multi-sensory-features.
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