Multisensor data fusion for gearbox fault diagnosis using 2-D convolutional neural network and motor current signature analysis

Gearboxes are widely used in rotating machinery and various industrial applications for transmission of power and torque. They operate for prolong hours and under different working conditions which may increase their probability of failure. Sudden failure of a gearbox may lead to significant downtime and increase maintenance costs. In industrial applications, usually fault detection and diagnosis techniques based on vibration signal are used for monitoring the health condition of gearboxes. In most of these techniques, time and frequency domain features are manually extracted from a vibration sensor and used for fault detection and diagnosis. In this research, a fault diagnosis methodology based on motor current signature analysis is proposed. The acquired data from multiple current sensors are fused by a novel 2-D convolutional neural network architecture and used for classification purpose directly without any need for manual feature extraction. Performance of the proposed method has been evaluated on the motor current data obtained from an industrial gearbox test rig in various health condition and with different working speeds. In comparison with classical machine learning (ML) algorithms, the presented methodology exhibits the best classification performance for gearbox fault detection and diagnosis.