A wear particle identification method by combining principal component analysis and grey relational analysis

The process to identify wear particles concerns a variety of parameters, some of which may be redundant, and influences the efficiency of computer image analysis. In order to improve the accuracy and speed of debris identification, this paper proposes a new algorithm that combines principal component analysis and grey relational analysis (CPGA). First, principal component analysis is used to optimise the characteristic parameters of wear particles. Then, an improved grey relational analysis is used to discriminate between similar types of wear particles, such as severe sliding and fatigue particles. The experimental results indicate that the CPGA algorithm can successfully solve the information redundancy problem resulting from multiple parameters and proves to be a practical method to identify wear particles quickly and accurately.