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

M. A. Yudha; I. Surjandari

doi:10.1088/1757-899X/598/1/012088

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

M. A. Yudha, I. Surjandari, Zulkarnain

Department of Industrial Engineering

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Article number	012088
Journal	IOP Conference Series: Materials Science and Engineering
Volume	598
Issue number	1
DOIs	https://doi.org/10.1088/1757-899X/598/1/012088
Publication status	Published - 9 Sept 2019
Event	Joint Conference of the 6th Annual Conference of Industrial and System Engineering 2019, ACISE 2019 and 1st International Conference on Risk Management as an Interdisciplinary Approach 2019, ICRMIA 2019 - Semarang, Central Java, Indonesia Duration: 23 Apr 2019 → 24 Apr 2019

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10.1088/1757-899X/598/1/012088

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

abstract = "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.",

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AU - Yudha, M. A.

AU - Surjandari, I.

AU - Zulkarnain,

N1 - Publisher Copyright: © Published under licence by IOP Publishing Ltd.

PY - 2019/9/9

Y1 - 2019/9/9

N2 - 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.

AB - 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.

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DO - 10.1088/1757-899X/598/1/012088

M3 - Conference article

AN - SCOPUS:85073198277

SN - 1757-8981

VL - 598

JO - IOP Conference Series: Materials Science and Engineering

JF - IOP Conference Series: Materials Science and Engineering

IS - 1

M1 - 012088

T2 - Joint Conference of the 6th Annual Conference of Industrial and System Engineering 2019, ACISE 2019 and 1st International Conference on Risk Management as an Interdisciplinary Approach 2019, ICRMIA 2019

Y2 - 23 April 2019 through 24 April 2019

ER -

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

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