Method for Identifying Materials and Sizes of Particles Based on Neural Network

Abstract

Ships are equipped with power plants and operational assistance devices, both of which need oil for lubrication or energy transfer. Oil carries a large number of metal particles. By identifying the materials and sizes of metal particles in oil, the position and type of wear can be fully understood. However, existing online oil-detection methods make it difficult to identify the materials and the sizes of metal particles simultaneously and continuously. In this paper, we proposed a method for identifying the materials and the sizes of particles based on neural network. Firstly, a tree network model was designed. Then, each sub-network was trained in stages. Finally, the identification performance of several key groups of different frequencies and frequency combinations was tested. The experimental results showed that the method was effective. The accuracies of material and size identification reached 98% and 95% in the pre-training stage, and both had strong robustness.

Keywords

• metal particle identification
• neural network
• pre-training
• autoencoder