This paper presents a method for automated bearing fault detection via motor current analysis using Long Short-Term Memory networks. Minimal pre-processing is applied to current signals. The proposed approach is experimentally validated on a laboratory trial comprising different test sets for condition monitoring and fault diagnosis of a 6-poles induction motor. Preliminary results confirmed the effectiveness of the proposed method to detect various bearing faults under different operating conditions, such as: shaft radial load and output torque.
Automated Bearing Fault Detection via Long Short-Term Memory Networks / Immovilli F.; Lippi M.; Cocconcelli M.. - ELETTRONICO. - (2019), pp. 452-458. (Intervento presentato al convegno 12th IEEE International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives, SDEMPED 2019 tenutosi a fra nel 2019) [10.1109/DEMPED.2019.8864866].
Automated Bearing Fault Detection via Long Short-Term Memory Networks
Lippi M.;
2019
Abstract
This paper presents a method for automated bearing fault detection via motor current analysis using Long Short-Term Memory networks. Minimal pre-processing is applied to current signals. The proposed approach is experimentally validated on a laboratory trial comprising different test sets for condition monitoring and fault diagnosis of a 6-poles induction motor. Preliminary results confirmed the effectiveness of the proposed method to detect various bearing faults under different operating conditions, such as: shaft radial load and output torque.
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