|
A method for analyzing the morphological characteristics of wear particles in marine cylinder lubrication is proposed. The method uses a BP neural network recognition algorithm to identify these particles. The process involves correcting, filtering, binarizing, and processing typical wear particle images through morphological operations. Statistical and Fourier feature parameters extract size and shape characteristics of the wear images, while gray co-occurrence matrix extracts texture feature parameters. Ultimately, the method establishes a wear recognition system based on the BP neural network. Based on the findings, using a sharpened filter and automatic threshold binarization can enhance the clarity of the texture and boundary of the grinding particle. Additionally, performing open and close operations can eliminate any remaining holes and broken points in the image morphology processing, resulting in a smoother edge for the grinding particle. Furthermore, the BP neural network model is capable of automatically identifying the form of cylinder lubrication particles, determining the wear mechanism, and adjusting the cylinder oil filling rate by utilizing the detection results of cylinder residual oil.
|
