Deep convolutional neural network based on adaptive gradient optimizer for fault detection in SCIM

Early fault detection in squirrel cage induction motor (SCIM) can minimize the downtime and maximize production. This paper presents an adaptive gradient optimizer based deep convolutional neural network (ADG-dCNN) technique for bearing and rotor faults detection in squirrel cage induction motor. Multiple MEMS accelerometers have been used for vibration data collection, and sensor data fusion is employed in the model training and testing. ADG-dCNN allows the automatic feature extraction from the vibration data and minimizes the need for human expertise and reduces human intervention. It eliminates the error caused by manual feature extraction and selection, which is dependent on prior knowledge of fault types. This paper presents an end-to-end learning fault detection system based on deep CNN. The dataset for training and testing of the proposed method is generated from the test setup. The proposed classifier attained an average accuracy of 99.70%. This paper also presents the recently developed SHapley Additive exPlanations (SHAP) methodology for evaluation of fault classification from the proposed model. The proposed technique can also be extended to other machinery with multiple sensors owing to its end-to-end learning abilities. (C)2020 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

Early fault detection in squirrel cage induction motor is crucial for minimizing downtime and maximizing production. This paper presents an adaptive gradient optimizer based deep convolutional neural network (ADG-dCNN) technique for bearing and rotor faults detection in squirrel cage induction motor. The proposed model attained an average accuracy of 99.70% and can be extended to other machinery with multiple sensors.