SUMMARY OF THE RECENT DEVELOPED TECHNIQUES FOR MACHINE HEALTH PROGNOSTICS
ABSTRACT: This paper
reviews relatively new
developed techniques for
machine health prognostics
system. The prognostics assessment
of machines is an important
consideration for determining
the remaining useful life
(RUL) of machine
components and prediction
of future state
of machines. The
developed system has employed several approaches of machine health
prognostics strategy such as data-driven, physical-based, and
probability-based methods. The
method of solution
implemented artificial intelligence techniques
including support vector
machine (SVM), relevance
vector machine (RVM), Dempster-Shafer theory, decision tree,
particle filter, and autoregressive moving average/ generalized autoregressive conditional heteroscedasticity
(ARMA/GARCH). Case studies
of machine health prognostics are
also presented to
show the plausibility
of the developed
systems. Finally, this
paper summarizes the research finding and directions of machine health
prognostics system.
Author: Achmad Widodo, Wahyu
Caesarendra
Journal Code: jptmesingg140004
