Feature Extraction o Condition Monitoring Data on Heavy Equipment's Component Using Principal Component Analysis (PCA)

Annual Conference on Industrial and System Engineering 2019

工业技术（总论）

Yudha, M.A.^1 ; Surjandari, I.^1 ; Zulkarnain^1

Department of Industrial Engineering, Universitas Indonesia, Depok

16424, Indonesia^1

关键词: Component failures;    Correlated variables;    Heavy equipment;    Maintenance strategies;    Mechanical transmission;    O-conditions;    Principal Components;    Wear particles;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/598/1/012088/pdf
DOI  :  10.1088/1757-899X/598/1/012088

学科分类：工业工程学

来源: IOP

【 摘 要 】

The maintenance strategy is significantly important to minimize risk and impact on equipment productivity from component failure. The mechanical transmission on heavy equipment has a function to change speed and torque from engine to final drive. Because of the function that carries high loads which leads to an increase in wear particles, a condition monitoring (CM) approaches is employed. CM data is consisting of 26 parameters and need to reduce the dimension for simplifying correlated variables into fewer independent principal components (PCs). Principal component analysis (PCA) method has been applied to this dataset and deciding 10 PCs with explaining 73.62% variability of the data.