Classification and spectrum optimization method of grease based on infrared spectrum

Abstract

The infrared (IR) absorption spectral data of 63 kinds of lubricating greases containing six different types of thickeners were obtained using the IR spectroscopy. The Kohonen neural network algorithm was used to identify the type of the lubricating grease. The results show that this machine learning method can effectively eliminate the interference fringes in the IR spectrum, and complete the feature selection and dimensionality reduction of the high-dimensional spectral data. The 63 kinds of greases exhibit spatial clustering under certain IR spectrum recognition spectral bands, which are linked to characteristic peaks of lubricating greases and improve the recognition accuracy of these greases. The model achieved recognition accuracy of 100.00%, 96.08%, 94.87%, 100.00%, and 87.50% for polyurea grease, calcium sulfonate composite grease, aluminum (Al)-based grease, bentonite grease, and lithium-based grease, respectively. Based on the different IR absorption spectrum bands produced by each kind of lubricating grease, the three-dimensional spatial distribution map of the lubricating grease drawn also verifies the accuracy of classification while recognizing the accuracy. This paper demonstrates fast recognition speed and high accuracy, proving that the Kohonen neural network algorithm has an efficient recognition ability for identifying the types of the lubricating grease.