  It has been challenging to identify ferrograph images with a small dataset
and various scales of wear particle. A novel model is proposed in this study to
cope with these challenging problems. For the problem of insufficient samples,
we first proposed a data augmentation algorithm based on the permutation of
image patches. Then, an auxiliary loss function of image patch permutation
recognition was proposed to identify the image generated by the data
augmentation algorithm. Moreover, we designed a feature extraction loss
function to force the proposed model to extract more abundant features and to
reduce redundant representations. As for the challenge of large change range of
wear particle size, we proposed a multi-scale feature extraction block to
obtain the multi-scale representations of wear particles. We carried out
experiments on a ferrograph image dataset and a mini-CIFAR-10 dataset.
Experimental results show that the proposed model can improve the accuracy of
the two datasets by 9% and 20% respectively compared with the baseline.
