Fault States Diagnosis of Marine Diesel Engine Valve Based on a Modified VGG16 Transfer Learning Method

Abstract

The marine diesel engine is an important power machine for ships. Traditional machine learning methods for diesel engine fault diagnosis usually require a large amount of labeled training data, and the diagnosis performance may decline when encounters vibrational and environmental interference. A transfer learning convolutional neural network model based on VGG16 is introduced for diesel engine valve leakage fault diagnosis. The acquired diesel engine cylinder head vibration signal is first converted to time domain, frequency domain, and wavelet decomposition images. Secondly, the VGG16 deep convolutional neural network is pretrained using the ImageNet dataset. Subsequently, fine tuning the network based on the pretrained basic parameters and image enhancement methods. Finally, the well-trained model is adopted to train and test the target dataset. In addition, the cosine annealing learning rate setting method is used to make the learning rate close to the global optimal solution. Experimental results show that the proposed method has higher accuracy and better robustness against noise with a small sample dataset than traditional methods and deep learning models. This study not only demonstrates a novel view for the diagnosis of marine diesel engine valve leakage, but also provides an applicable diagnosis method for other similar issues.