@inproceedings{scholars9505,
           title = {Application of Principal Component Analysis vs. Multiple Linear Regression in Resolving Influential Factor Subject to Air Booster Compressor Motor Failure},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
         journal = {2018 IEEE 4th International Symposium in Robotics and Manufacturing Automation, ROMA 2018},
             doi = {10.1109/ROMA46407.2018.8986722},
            year = {2018},
            note = {cited By 3; Conference of 4th IEEE International Symposium in Robotics and Manufacturing Automation, ROMA 2018 ; Conference Date: 10 December 2018 Through 12 December 2018; Conference Code:157694},
            isbn = {9781728103747},
          author = {Rosli, N. S. B. and Ibrahim, R. B. and Ismail, I. and Bingi, K.},
        abstract = {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. {\^A}{\copyright} 2018 IEEE.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85080025309&doi=10.1109\%2fROMA46407.2018.8986722&partnerID=40&md5=d00c5b6f589fa187742610f3719ad49b},
        keywords = {Failure (mechanical); Gas compressors; Linear regression; Manufacture; Plant shutdowns; Predictive maintenance; Robotics, Booster compressor; Equipment reliability; Industrial motor; Influential factors; Motor failure; Multiple linear regressions, Principal component analysis}
}