relation: https://khub.utp.edu.my/scholars/9505/
title: Application of Principal Component Analysis vs. Multiple Linear Regression in Resolving Influential Factor Subject to Air Booster Compressor Motor Failure
creator: Rosli, N.S.B.
creator: Ibrahim, R.B.
creator: Ismail, I.
creator: Bingi, K.
description: Predictive maintenance is vital towards the industrial economy to improve equipment reliability, efficiency and reduce downtime. The main objective of this work was to investigate the most influential factors contributing to the failure of the industrial motor to improve predictive maintenance. The most significant method was employed to investigate the most influential factors that affect the prediction of motor failure. There are 14 parameters were used to assess the most influential factors to the failure of the ABC motor. The Multiple Linear Regression (MLR) and Principal Component Analysis (PCA) was carried out to evaluate the best influential factor to the failure. The result revealed the group of parameters that influence the ABC motor failure. However, the finding can be used as a guideline for predictive maintenance in order to mitigate the risk of the plant shutdown. © 2018 IEEE.
publisher: Institute of Electrical and Electronics Engineers Inc.
date: 2018
type: Conference or Workshop Item
type: PeerReviewed
identifier:   Rosli, N.S.B. and Ibrahim, R.B. and Ismail, I. and Bingi, K.  (2018) Application of Principal Component Analysis vs. Multiple Linear Regression in Resolving Influential Factor Subject to Air Booster Compressor Motor Failure.  In: UNSPECIFIED.     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080025309&doi=10.1109%2fROMA46407.2018.8986722&partnerID=40&md5=d00c5b6f589fa187742610f3719ad49b
relation: 10.1109/ROMA46407.2018.8986722
identifier: 10.1109/ROMA46407.2018.8986722