This document presents a research paper that proposes a deep convolutional neural network-based transfer learning approach for gear fault diagnosis using small datasets. The approach consists of two parts: 1) a pre-trained deep neural network that serves to automatically extract features from vibration data inputs, and 2) a fully connected stage to classify the extracted features, which is trained using a small set of experimental gear fault data. The study aims to develop a preprocessing-free method for gear fault diagnosis that can adaptively extract features and handle small training datasets using transfer learning techniques. Experimental results on a benchmark gear system indicate the proposed approach achieves both goals.

Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning PDF

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