Automated Bearing Fault Detection via Long Short-Term Memory Networks
2019
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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