Wear particles image enhancement using long short-term memory 3D recurrent reconstruction neural network (LSTM 3D-R2N2)

Abstract

3D modeling of wear particles has proven to be a useful tool for monitoring mechanical failure conditions. In this work, a new method for 3D reconstruction of wear particles in uncontaminated oil (healthy oil) and contaminated oil (used oil) was proposed. The image acquisition device can capture multi-view images of moving wear particles in both healthy and used oil by using the reflected light. The images were pretreated first, and the image color inversion was conducted using the Pillow library. The pretreated wear particle images were used for 3D reconstruction using long short-term memory 3D recurrent reconstruction neural network. The current results were verified against existing results, and good agreement can be found. It can be concluded that we can reconstruct the similar 3D wear particle results with fewer images by comparison with other methods. Specifically, only 4–6 image samples were used for the 3D reconstruction of wear particles, and at least 8 image samples were needed for other existing reports.