  This paper provides an economic perspective on the predictive maintenance of
filtration units. The rise of predictive maintenance is possible due to the
growing trend of industry 4.0 and the availability of inexpensive sensors.
However, the adoption rate for predictive maintenance by companies remains low.
The majority of companies are sticking to corrective and preventive
maintenance. This is not due to a lack of information on the technical
implementation of predictive maintenance, with an abundance of research papers
on state-of-the-art machine learning algorithms that can be used effectively.
The main issue is that most upper management has not yet been fully convinced
of the idea of predictive maintenance. The economic value of the implementation
has to be linked to the predictive maintenance program for better justification
by the management. In this study, three machine learning models were trained to
demonstrate the economic value of predictive maintenance. Data was collected
from a testbed located at the Singapore University of Technology and Design.
The testbed closely resembles a real-world water treatment plant. A
cost-benefit analysis coupled with Monte Carlo simulation was proposed. It
provided a structured approach to document potential costs and savings by
implementing a predictive maintenance program. The simulation incorporated
real-world risk into a financial model. Financial figures were adapted from
CITIC Envirotech Ltd, a leading membrane-based integrated environmental
solutions provider. Two scenarios were used to elaborate on the economic values
of predictive maintenance. Overall, this study seeks to bridge the gap between
technical and business domains of predictive maintenance.
