Gas-to-liquid lubricant identification technology based on mutual information and Bayesian algorithm

Objective A novel technique for the identification of gas-to-liquid (GTL) lubricants based on mutual information method and Bayesian algorithm is proposed to enhance the tariff identification of lubricating oils.

Methods First, feature parameters of GTL lubricants and conventional petroleum lubricants are extracted, and the mutual information algorithm is used to select feature indicators with discriminative ability. Then, the Bayesian algorithm is used to model the selected feature indicators, which finally achieves the classification discrimination of GTL lubricants and conventional lubricants.

Results The experimental results demonstrate the effectiveness of the proposed method in accurately discriminating between GTL lubricants and conventional petroleum lubricants.

Conclusions The established model exhibits good stability and reliability, providing a comprehensive and accurate identification tool for the lubricating oil industry and tariff assessment.