Intelligent Recognition of Ferrographic Images Combining Optimal CNN With Transfer Learning Introducing Virtual Images

Ferrography analysis(FA) is an important approach to detect the wear state of mechanical equipment. Ferrographic image recognition based on deep learning needs a large number of image samples. However, the ferrographic images of mechanical equipment are difficult to obtain enough high-quality samples in a short time due to the complexity and low efficiency of the ferrogram making. Therefore, the recognition method for small sample ferrographic images based on the convolutional neural network(CNN) and transfer learning(TL) is proposed. Based on the similarity of samples, the virtual ferrographic image set is designed as the source data of the pretraining model, the tested CNN model is constructed by using the TL. Based on the AlexNet frame, this paper studies the influence of the CNN internal factors including network structure, convolution parameters, activation function, optimization mode, learning rate and the external factors on the classification effect of test samples, and the L2 regularizer is added to solve the overfitting. According to the classification result of test samples, an optimal parameter combination is obtained to establish an intelligent recognition model of ferrographic images based on CNN and TL with the recognition accuracy of 93.75%. Moreover, the t-SNE is used to realize the wear particle recognition process visualization, which proves the effectiveness of the proposed algorithm. This work provides an effective way for the ferrographic image recognition of wear particles under small samples.