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

• Published in: Wear 2013-07, Vol.304 (1-2), p.96-102
• Main Authors: Wang, Jingqiu, Wang, Xiaolei
• Format: Article
• Language: English
• Subjects: Algorithms; Debris; Exact sciences and technology; Fatigue (materials); Ferrography; Fundamental areas of phenomenology (including applications); Grey relational analysis; Image analysis; Mechanical contact (friction...); Physics; Principal component analysis; Redundant; Solid mechanics; Structural and continuum mechanics; Wear; Wear particle identification; Wear particles
• ISSN: 0043-1648; 1873-2577
• DOI: 10.1016/j.wear.2013.04.021

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. •Wear particles identification by principal component and grey relational analysis.•Solve the redundant problem of debris parameters by principal component analysis.•Obtain objective debris identification by improved grey relational analysis.