An oil wear particle identification method based on Wasserstein generative adversarial network and improved CNN using a custom-built optical imaging sensor

Abstract

The accurate classification of wear particles in lubricating oil holds significant importance for understanding the wear characteristics of lubricated components. However, there are prominent issues, such as blurred particle images, limited identification efficiency, and low generalization capabilities. To solve such issues, this research initially devises a novel online monitoring sensor to collect particle images based on the optical method. Subsequently, an improved Wasserstein Generative Adversarial Network (WGAN) is developed to ameliorate the categorization performance, which is hampered by data imbalance. Lastly, a MobileNetV2-SENet-based Convolutional Neural Network (MSCNN) is proposed to autonomously discover the associations between images and types of wear particles. Experimental results illustrate that our method achieves a recognition accuracy rate of 92.5%. The analysis outcomes between several comparative networks show that our approach can effectively improve recognition performance when limited samples are available. This work offers an effective solution for automatically identifying wear particles in oil and can be a powerful predictive maintenance tool for rotating mechanical equipment.