Conditional generative adversarial network based data augmentation for fault diagnosis of diesel engines applied with infrared thermography and deep convolutional neural network

Abstract

This paper tries to introduce a new intelligent method for the early fault diagnosis of diesel engines. Firstly, infrared thermography (IRT) is introduced into diesel engine condition monitoring, then infrared images of diesel engines in four health states, such as normal condition, single-cylinder misfire, multi-cylinder misfire and air filter blockage, are collected and the region of interest (ROI) of infrared images are extracted. Next, conditional generative adversarial network (CGAN) is deployed to perform data augmentation on infrared image datasets. Then, deep convolutional neural network (DCNN) and Softmax regression (SR) classifier are used for automatically extracting infrared image fault features and pattern recognition, respectively. Finally, a comparison with three deep learning (DL) models is performed. The validation results show that the data augmentation method proposed in the paper can significantly improve the early fault diagnosis accuracy, and DCNN has the best fault diagnosis effect and resistance to temperature fluctuation interference among the four DL models.