A transfer learning-based deep convolutional neural network approach for induction machine multiple faults detection

Abstract

The condition monitoring of squirrel cage induction motors (SCIMs) is vital for uninterrupted production and minimum downtime. Early fault detection can boost output with minimum effort. This article combines the application of transfer learning and convolution neural network (TL-CNN) for developing an efficient model for bearing and rotor broken bars damage identification in SCIMs. A simple technique for the 1-D current signal-to-image conversion is also proposed to provide input to the proposed deep learning-based TL-CNN technique. The proposed approach embodies the advantages of TL and CNN for effective fault identification in SCIMs. The developed technique has classified faults efficiently with an average accuracy of 99.40%. The complete analysis and data collection have been done on the experimental set-up with a 5 kW SCIM and LabVIEW-based data acquisition system. The propounded fault detection model has been created in python with the help of packages like Keras and TensorFlow.