A Novel Hybrid Signal Decomposition Technique for Transfer Learning Based Industrial Fault Diagnosis

Abstract

In the fourth industrial revolution, data-driven intelligent fault diagnosis for industrial purposes serves a crucial role. In contemporary times, although deep learning is a popular approach for fault diagnosis, it requires massive amounts of labelled samples for training, which is arduous to come by in the real world. Our contribution to introduce a novel comprehensive intelligent fault detection model using the Case Western Reserve University dataset is divided into two steps. Firstly, a new hybrid signal decomposition methodology is developed comprising Empirical Mode Decomposition and Variational Mode Decomposition to leverage signal information from both processes for effective feature extraction. Secondly, transfer learning with DenseNet121 is employed to alleviate the constraints of deep learning models. Finally, our proposed novel technique surpassed not only previous outcomes but also generated state-of-the-art outcomes represented via the F1 score.

Three-Dimensional Printing Quality Inspection Based on Transfer Learning with Convolutional Neural Networks

“…This led to the creation of the ResNet series [ 31 , 32 ]. ResNet has been used for fault diagnosis in industrial manufacturing [ 33 , 34 ], rolling bearings [ 35 ], and rotating machinery [ 36 ]. The increases in network depth and width facilitated by the ResNet architecture led to tremendous improvements in CNN performance.…”