Open AccessMachinery Faults Prediction Using Ensemble Tree Classifiers: Bagging or Boosting?
Author(s) -
Somayeh Bakkhtiari Ramezani,
Amin Amirlatifi,
Thomas J. Kirby,
Shahram Rahimi,
Maria Seale
Publication year - 2021
Publication title -
proceedings of the annual conference of the prognostics and health management society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.18
H-Index - 11
ISSN - 2325-0178
DOI - 10.36001/phmconf.2021.v13i1.3063
Subject(s) - boosting (machine learning) , computer science , robustness (evolution) , root cause , root cause analysis , data mining , fault tree analysis , decision tree , machine learning , artificial intelligence , identification (biology) , reliability engineering , engineering , biochemistry , chemistry , botany , biology , gene
One of the main goals of predictive maintenance is to accurately classify the temporal trends as early as possible, detect faulty states and pinpoint the root cause of the fault. Undoubtedly, neither late nor early maintenance is desirable and incurs additional operating costs; however, early identification of faulty trends and scheduling on-time maintenance is crucial to smooth machinery operation. Various data-driven techniques have been used to identify faults; nevertheless, many of these techniques fail to perform when faced with missing values at run time or lack any explanation on the root cause of the fault. The present work offers a comprehensive study on different techniques used for fault type classification and compares their performance in identifying the mode of operation for the PHME21 dataset. We also evaluate the robustness of such classifiers against missing values. This study shows that tree-based techniques are best suited to perform root cause analysis for each faulty state and establish rules for faulty conditions.