Ferrograph image classification
This work addresses the problem of ferrograph image classification for industrial monitoring applications, representing an incremental advancement with specific gains.
The study tackled the challenge of classifying ferrograph images with limited data and varying wear particle scales by proposing a novel model that includes data augmentation, auxiliary loss functions, and multi-scale feature extraction, resulting in accuracy improvements of 9% on a ferrograph dataset and 20% on mini-CIFAR-10 compared to baselines.
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.