This paper explores a strategy for fault detection and recognition in a high-production horizontal broaching machine used for manufacturing lock components. The experimental campaign involved inducing artificial faults and measuring vibration signals by means of four triaxial accelerometers. The main challenges included the unknown elastodynamic response of the machine and limited data availability due to time constraints. To overcome these issues, statistical parameters were computed from shorter portions of measurements, and datasets were expanded by combining these portions through computation of cross-correlation functions. Feature vectors were extracted and labelled according to the tool’s health state. Supervised classification algorithms, including K-NN, SVM, ANN, and Random Forest, were applied. Despite the severe artificially induced faults, the method successfully distinguished between healthy and faulty states, though further investigation is needed to enhance accuracy for realistic faults.

Rosa, S., Martini, A., Rivola, A., Troncossi, M. (2024). Condition monitoring of a broaching machine through machine learning classification of a limited dataset vibration signals. Leuven : KU Leuven, Departement Werktuigkunde.

Condition monitoring of a broaching machine through machine learning classification of a limited dataset vibration signals

Rosa S.;Martini A.;Rivola A.;Troncossi M.
2024

Abstract

This paper explores a strategy for fault detection and recognition in a high-production horizontal broaching machine used for manufacturing lock components. The experimental campaign involved inducing artificial faults and measuring vibration signals by means of four triaxial accelerometers. The main challenges included the unknown elastodynamic response of the machine and limited data availability due to time constraints. To overcome these issues, statistical parameters were computed from shorter portions of measurements, and datasets were expanded by combining these portions through computation of cross-correlation functions. Feature vectors were extracted and labelled according to the tool’s health state. Supervised classification algorithms, including K-NN, SVM, ANN, and Random Forest, were applied. Despite the severe artificially induced faults, the method successfully distinguished between healthy and faulty states, though further investigation is needed to enhance accuracy for realistic faults.
2024
Proceedings of ISMA 2024 - International Conference on Noise and Vibration Engineering and USD 2024 - International Conference on Uncertainty in Structural Dynamics
3055
3068
Rosa, S., Martini, A., Rivola, A., Troncossi, M. (2024). Condition monitoring of a broaching machine through machine learning classification of a limited dataset vibration signals. Leuven : KU Leuven, Departement Werktuigkunde.
Rosa, S.; Martini, A.; Rivola, A.; Troncossi, M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11585/1000842
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