• Investigated the possibility of predictive maintenance of an ultra-filtration unit by adopting machine learning and data analytics to predict the differential pressure
• Performed 5 attack scenarios on the secure water treatment test-bed and collected the sensor data for model development
• Analyzed the financial impact of Industry 4.0 levers such as IoT enabled sensors on businesses
• Utilized Monte Carlo simulation to model risk and uncertainty for 4 cost savings categories due to model instability
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.
I would like to acknowledge the funding support from Nanyang Technological University – URECA Undergraduate Research Programme for this research project.
Supervisor: Assoc Prof Law Wing-Keung, Adrian
Python: Version 3.6
Packages: pandas, numpy, matplotlib, seaborn, sklearn, xgboost
Matlab: MATLAB® Online™
Rockwell Software: RSLogix 5000
Datasets: Requested available data at https://itrust.sutd.edu.sg/itrust-labs_datasets/ / collected own data based on required scenarios
Research Paper Preprint: https://www.researchgate.net/publication/343537623_An_Economic_Perspective_on_Predictive_Maintenance_of_Filtration_Units